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Merge branch 'develop' into feature/NoiseModelFactorN 

#1102 correctly removed "GTSAM_EXPORT" from NoiseModelFactorX
release/4.3a0
Gerry Chen 2022-04-21 15:52:35 -04:00
parent 782a894988 3d2079237c
commit 8ae8c7ac52
477 changed files with 31278 additions and 8347 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

13
.github/scripts/unix.sh vendored
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@ -71,6 +71,7 @@ function configure()
-DGTSAM_USE_SYSTEM_EIGEN=${GTSAM_USE_SYSTEM_EIGEN:-OFF} \
-DGTSAM_USE_SYSTEM_METIS=${GTSAM_USE_SYSTEM_METIS:-OFF} \
-DGTSAM_BUILD_WITH_MARCH_NATIVE=OFF \
-DGTSAM_SINGLE_TEST_EXE=ON \
-DBOOST_ROOT=$BOOST_ROOT \
-DBoost_NO_SYSTEM_PATHS=ON \
-DBoost_ARCHITECTURE=-x64
@ -95,7 +96,11 @@ function build ()
configure
if [ "$(uname)" == "Linux" ]; then
make -j$(nproc)
if (($(nproc) > 2)); then
make -j$(nproc)
else
make -j2
fi
elif [ "$(uname)" == "Darwin" ]; then
make -j$(sysctl -n hw.physicalcpu)
fi
@ -113,7 +118,11 @@ function test ()
# Actual testing
if [ "$(uname)" == "Linux" ]; then
make -j$(nproc) check
if (($(nproc) > 2)); then
make -j$(nproc) check
else
make -j2 check
fi
elif [ "$(uname)" == "Darwin" ]; then
make -j$(sysctl -n hw.physicalcpu) check
fi

21
.github/workflows/build-windows.yml vendored
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@ -48,7 +48,9 @@ jobs:
- name: Install Dependencies
shell: powershell
run: |
Invoke-Expression (New-Object System.Net.WebClient).DownloadString('https://get.scoop.sh')
iwr -useb get.scoop.sh -outfile 'install_scoop.ps1'
.\install_scoop.ps1 -RunAsAdmin
scoop install cmake --global # So we don't get issues with CMP0074 policy
scoop install ninja --global
@ -106,6 +108,21 @@ jobs:
cmake --build build -j 4 --config ${{ matrix.build_type }} --target gtsam
cmake --build build -j 4 --config ${{ matrix.build_type }} --target gtsam_unstable
cmake --build build -j 4 --config ${{ matrix.build_type }} --target wrap
# Run GTSAM tests
cmake --build build -j 4 --config ${{ matrix.build_type }} --target check.base
cmake --build build -j 4 --config ${{ matrix.build_type }} --target check.base_unstable
cmake --build build -j 4 --config ${{ matrix.build_type }} --target check.basis
cmake --build build -j 4 --config ${{ matrix.build_type }} --target check.discrete
#cmake --build build -j 4 --config ${{ matrix.build_type }} --target check.geometry
cmake --build build -j 4 --config ${{ matrix.build_type }} --target check.inference
cmake --build build -j 4 --config ${{ matrix.build_type }} --target check.linear
cmake --build build -j 4 --config ${{ matrix.build_type }} --target check.navigation
#cmake --build build -j 4 --config ${{ matrix.build_type }} --target check.nonlinear
#cmake --build build -j 4 --config ${{ matrix.build_type }} --target check.sam
cmake --build build -j 4 --config ${{ matrix.build_type }} --target check.sfm
#cmake --build build -j 4 --config ${{ matrix.build_type }} --target check.slam
cmake --build build -j 4 --config ${{ matrix.build_type }} --target check.symbolic
# Run GTSAM_UNSTABLE tests
#cmake --build build -j 4 --config ${{ matrix.build_type }} --target check.base_unstable

1
.gitignore vendored
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@ -17,3 +17,4 @@
# for QtCreator:
CMakeLists.txt.user*
xcode/
/Dockerfile

8
CMakeLists.txt
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@ -1,4 +1,3 @@
project(GTSAM CXX C)
cmake_minimum_required(VERSION 3.0)
# new feature to Cmake Version > 2.8.12
@ -11,7 +10,7 @@ endif()
set (GTSAM_VERSION_MAJOR 4)
set (GTSAM_VERSION_MINOR 2)
set (GTSAM_VERSION_PATCH 0)
set (GTSAM_PRERELEASE_VERSION "a4")
set (GTSAM_PRERELEASE_VERSION "a6")
math (EXPR GTSAM_VERSION_NUMERIC "10000 * ${GTSAM_VERSION_MAJOR} + 100 * ${GTSAM_VERSION_MINOR} + ${GTSAM_VERSION_PATCH}")
if (${GTSAM_VERSION_PATCH} EQUAL 0)
@ -19,6 +18,11 @@ if (${GTSAM_VERSION_PATCH} EQUAL 0)
else()
set (GTSAM_VERSION_STRING "${GTSAM_VERSION_MAJOR}.${GTSAM_VERSION_MINOR}.${GTSAM_VERSION_PATCH}${GTSAM_PRERELEASE_VERSION}")
endif()
project(GTSAM
LANGUAGES CXX C
VERSION "${GTSAM_VERSION_MAJOR}.${GTSAM_VERSION_MINOR}.${GTSAM_VERSION_PATCH}")
message(STATUS "GTSAM Version: ${GTSAM_VERSION_STRING}")
set (CMAKE_PROJECT_VERSION_MAJOR ${GTSAM_VERSION_MAJOR})

2
DEVELOP.md
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@ -15,7 +15,7 @@ For example:
```cpp
class GTSAM_EXPORT MyClass { ... };
GTSAM_EXPORT myFunction();
GTSAM_EXPORT return_type myFunction();
```
More details [here](Using-GTSAM-EXPORT.md).

4
INSTALL.md
View File

@ -13,7 +13,7 @@ $ make install
## Important Installation Notes
1. GTSAM requires the following libraries to be installed on your system:
- BOOST version 1.65 or greater (install through Linux repositories or MacPorts). Please see [Boost Notes](#boost-notes).
- BOOST version 1.65 or greater (install through Linux repositories or MacPorts). Please see [Boost Notes](#boost-notes) for version recommendations based on your compiler.
- Cmake version 3.0 or higher
- Support for XCode 4.3 command line tools on Mac requires CMake 2.8.8 or higher
@ -72,7 +72,7 @@ execute commands as follows for an out-of-source build:
Versions of Boost prior to 1.65 have a known bug that prevents proper "deep" serialization of objects, which means that objects encapsulated inside other objects don't get serialized.
This is particularly seen when using `clang` as the C++ compiler.
For this reason we require Boost>=1.65, and recommend installing it through alternative channels when it is not available through your operating system's primary package manager.
For this reason we recommend Boost>=1.65, and recommend installing it through alternative channels when it is not available through your operating system's primary package manager.
## Known Issues

1
Using-GTSAM-EXPORT.md
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@ -8,6 +8,7 @@ To create a DLL in windows, the `GTSAM_EXPORT` keyword has been created and need
* At least one of the functions inside that class is declared in a .cpp file and not just the .h file.
* You can `GTSAM_EXPORT` any class it inherits from as well. (Note that this implictly requires the class does not derive from a "header-only" class. Note that Eigen is a "header-only" library, so if your class derives from Eigen, _do not_ use `GTSAM_EXPORT` in the class definition!)
3. If you have defined a class using `GTSAM_EXPORT`, do not use `GTSAM_EXPORT` in any of its individual function declarations. (Note that you _can_ put `GTSAM_EXPORT` in the definition of individual functions within a class as long as you don't put `GTSAM_EXPORT` in the class definition.)
4. For template specializations, you need to add `GTSAM_EXPORT` to each individual specialization.
## When is GTSAM_EXPORT being used incorrectly
Unfortunately, using `GTSAM_EXPORT` incorrectly often does not cause a compiler or linker error in the library that is being compiled, but only when you try to use that DLL in a different library. For example, an error in `gtsam/base` will often show up when compiling the `check_base_program` or the MATLAB wrapper, but not when compiling/linking gtsam itself. The most common errors will say something like:

6
cmake/GtsamBuildTypes.cmake
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@ -93,6 +93,10 @@ if(MSVC)
/wd4267 # warning C4267: 'initializing': conversion from 'size_t' to 'int', possible loss of data
)
add_compile_options(/wd4005)
add_compile_options(/wd4101)
add_compile_options(/wd4834)
endif()
# Other (non-preprocessor macros) compiler flags:
@ -187,7 +191,7 @@ endif()
if (NOT MSVC)
option(GTSAM_BUILD_WITH_MARCH_NATIVE "Enable/Disable building with all instructions supported by native architecture (binary may not be portable!)" ON)
if(GTSAM_BUILD_WITH_MARCH_NATIVE)
if(GTSAM_BUILD_WITH_MARCH_NATIVE AND (APPLE AND NOT CMAKE_SYSTEM_PROCESSOR STREQUAL "arm64"))
# Add as public flag so all dependant projects also use it, as required
# by Eigen to avid crashes due to SIMD vectorization:
list_append_cache(GTSAM_COMPILE_OPTIONS_PUBLIC "-march=native")

11
cmake/HandleMetis.cmake
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@ -21,7 +21,12 @@ if(GTSAM_USE_SYSTEM_METIS)
mark_as_advanced(METIS_LIBRARY)
add_library(metis-gtsam-if INTERFACE)
target_include_directories(metis-gtsam-if BEFORE INTERFACE ${METIS_INCLUDE_DIR})
target_include_directories(metis-gtsam-if BEFORE INTERFACE ${METIS_INCLUDE_DIR}
# gtsam_unstable/partition/FindSeparator-inl.h uses internal metislib.h API
# via extern "C"
$<BUILD_INTERFACE:${GTSAM_SOURCE_DIR}/gtsam/3rdparty/metis/libmetis>
$<BUILD_INTERFACE:${GTSAM_SOURCE_DIR}/gtsam/3rdparty/metis/GKlib>
)
target_link_libraries(metis-gtsam-if INTERFACE ${METIS_LIBRARY})
endif()
else()
@ -30,10 +35,12 @@ else()
add_subdirectory(${GTSAM_SOURCE_DIR}/gtsam/3rdparty/metis)
target_include_directories(metis-gtsam BEFORE PUBLIC
$<INSTALL_INTERFACE:include/gtsam/3rdparty/metis/>
$<BUILD_INTERFACE:${GTSAM_SOURCE_DIR}/gtsam/3rdparty/metis/include>
# gtsam_unstable/partition/FindSeparator-inl.h uses internal metislib.h API
# via extern "C"
$<BUILD_INTERFACE:${GTSAM_SOURCE_DIR}/gtsam/3rdparty/metis/libmetis>
$<BUILD_INTERFACE:${GTSAM_SOURCE_DIR}/gtsam/3rdparty/metis/GKlib>
$<INSTALL_INTERFACE:include/gtsam/3rdparty/metis/>
)
add_library(metis-gtsam-if INTERFACE)

8
doc/Code/LocalizationExample2.cpp
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@ -1,7 +1,7 @@
// add unary measurement factors, like GPS, on all three poses
noiseModel::Diagonal::shared_ptr unaryNoise =
auto unaryNoise =
noiseModel::Diagonal::Sigmas(Vector2(0.1, 0.1)); // 10cm std on x,y
graph.add(boost::make_shared<UnaryFactor>(1, 0.0, 0.0, unaryNoise));
graph.add(boost::make_shared<UnaryFactor>(2, 2.0, 0.0, unaryNoise));
graph.add(boost::make_shared<UnaryFactor>(3, 4.0, 0.0, unaryNoise));
graph.emplace_shared<UnaryFactor>(1, 0.0, 0.0, unaryNoise);
graph.emplace_shared<UnaryFactor>(2, 2.0, 0.0, unaryNoise);
graph.emplace_shared<UnaryFactor>(3, 4.0, 0.0, unaryNoise);

7
doc/Code/LocalizationFactor.cpp
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@ -1,13 +1,12 @@
class UnaryFactor: public NoiseModelFactor1<Pose2> {
double mx_, my_; ///< X and Y measurements
public:
UnaryFactor(Key j, double x, double y, const SharedNoiseModel& model):
NoiseModelFactor1<Pose2>(model, j), mx_(x), my_(y) {}
Vector evaluateError(const Pose2& q,
boost::optional<Matrix&> H = boost::none) const
{
Vector evaluateError(const Pose2& q,
boost::optional<Matrix&> H = boost::none) const override {
const Rot2& R = q.rotation();
if (H) (*H) = (gtsam::Matrix(2, 3) <<
R.c(), -R.s(), 0.0,

8
doc/Code/OdometryExample.cpp
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@ -3,13 +3,11 @@ NonlinearFactorGraph graph;
// Add a Gaussian prior on pose x_1
Pose2 priorMean(0.0, 0.0, 0.0);
noiseModel::Diagonal::shared_ptr priorNoise =
noiseModel::Diagonal::Sigmas(Vector3(0.3, 0.3, 0.1));
graph.addPrior(1, priorMean, priorNoise);
auto priorNoise = noiseModel::Diagonal::Sigmas(Vector3(0.3, 0.3, 0.1));
graph.add(PriorFactor<Pose2>(1, priorMean, priorNoise));
// Add two odometry factors
Pose2 odometry(2.0, 0.0, 0.0);
noiseModel::Diagonal::shared_ptr odometryNoise =
noiseModel::Diagonal::Sigmas(Vector3(0.2, 0.2, 0.1));
auto odometryNoise = noiseModel::Diagonal::Sigmas(Vector3(0.2, 0.2, 0.1));
graph.add(BetweenFactor<Pose2>(1, 2, odometry, odometryNoise));
graph.add(BetweenFactor<Pose2>(2, 3, odometry, odometryNoise));

17
doc/Code/OdometryOutput1.txt
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@ -1,11 +1,14 @@
Factor Graph:
size: 3
factor 0: PriorFactor on 1
prior mean: (0, 0, 0)
Factor 0: PriorFactor on 1
prior mean: (0, 0, 0)
noise model: diagonal sigmas [0.3; 0.3; 0.1];
factor 1: BetweenFactor(1,2)
measured: (2, 0, 0)
noise model: diagonal sigmas [0.2; 0.2; 0.1];
factor 2: BetweenFactor(2,3)
measured: (2, 0, 0)
Factor 1: BetweenFactor(1,2)
measured: (2, 0, 0)
noise model: diagonal sigmas [0.2; 0.2; 0.1];
Factor 2: BetweenFactor(2,3)
measured: (2, 0, 0)
noise model: diagonal sigmas [0.2; 0.2; 0.1];

24
doc/Code/OdometryOutput2.txt
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@ -1,11 +1,23 @@
Initial Estimate:
Values with 3 values:
Value 1: (0.5, 0, 0.2)
Value 2: (2.3, 0.1, -0.2)
Value 3: (4.1, 0.1, 0.1)
Value 1: (gtsam::Pose2)
(0.5, 0, 0.2)
Value 2: (gtsam::Pose2)
(2.3, 0.1, -0.2)
Value 3: (gtsam::Pose2)
(4.1, 0.1, 0.1)
Final Result:
Values with 3 values:
Value 1: (-1.8e-16, 8.7e-18, -9.1e-19)
Value 2: (2, 7.4e-18, -2.5e-18)
Value 3: (4, -1.8e-18, -3.1e-18)
Value 1: (gtsam::Pose2)
(7.5-16, -5.3-16, -1.8-16)
Value 2: (gtsam::Pose2)
(2, -1.1-15, -2.5-16)
Value 3: (gtsam::Pose2)
(4, -1.7-15, -2.5-16)

18
doc/Code/OdometryOutput3.txt
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@ -1,12 +1,12 @@
x1 covariance:
0.09 1.1e-47 5.7e-33
1.1e-47 0.09 1.9e-17
5.7e-33 1.9e-17 0.01
0.09 1.7e-33 2.8e-33
1.7e-33 0.09 2.6e-17
2.8e-33 2.6e-17 0.01
x2 covariance:
0.13 4.7e-18 2.4e-18
4.7e-18 0.17 0.02
2.4e-18 0.02 0.02
0.13 1.2e-18 6.1e-19
1.2e-18 0.17 0.02
6.1e-19 0.02 0.02
x3 covariance:
0.17 2.7e-17 8.4e-18
2.7e-17 0.37 0.06
8.4e-18 0.06 0.03
0.17 8.6e-18 2.7e-18
8.6e-18 0.37 0.06
2.7e-18 0.06 0.03

254
doc/gtsam.lyx
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@ -134,14 +134,10 @@ A Hands-on Introduction
\begin_layout Author
Frank Dellaert
\begin_inset Newline newline
\end_inset
Technical Report number GT-RIM-CP&R-2014-XXX
\end_layout
\begin_layout Date
September 2014
Updated Last March 2022
\end_layout
\begin_layout Standard
@ -154,18 +150,14 @@ filename "common_macros.tex"
\end_layout
\begin_layout Section*
Overview
\end_layout
\begin_layout Standard
\begin_layout Abstract
In this document I provide a hands-on introduction to both factor graphs
and GTSAM.
This is an updated version from the 2012 TR that is tailored to our GTSAM
3.0 library and beyond.
4.0 library and beyond.
\end_layout
\begin_layout Standard
\begin_layout Abstract
\series bold
Factor graphs
@ -206,7 +198,7 @@ ts or prior knowledge.
robotics and vision.
\end_layout
\begin_layout Standard
\begin_layout Abstract
The GTSAM toolbox (GTSAM stands for
\begin_inset Quotes eld
\end_inset
@ -221,11 +213,13 @@ Georgia Tech Smoothing and Mapping
It provides state of the art solutions to the SLAM and SFM problems, but
can also be used to model and solve both simpler and more complex estimation
problems.
It also provides a MATLAB interface which allows for rapid prototype developmen
t, visualization, and user interaction.
It also provides MATLAB and Python wrappers which allow for rapid prototype
development, visualization, and user interaction.
In addition, it is easy to use in Jupyter notebooks and/or Google's coLaborator
y.
\end_layout
\begin_layout Standard
\begin_layout Abstract
GTSAM exploits sparsity to be computationally efficient.
Typically measurements only provide information on the relationship between
a handful of variables, and hence the resulting factor graph will be sparsely
@ -236,14 +230,17 @@ l complexity.
GTSAM provides iterative methods that are quite efficient regardless.
\end_layout
\begin_layout Standard
You can download the latest version of GTSAM at
\begin_layout Abstract
You can download the latest version of GTSAM from GitHub at
\end_layout
\begin_layout Abstract
\begin_inset Flex URL
status open
\begin_layout Plain Layout
http://tinyurl.com/gtsam
https://github.com/borglab/gtsam
\end_layout
\end_inset
@ -741,7 +738,7 @@ noindent
\begin_inset Formula $f_{0}(x_{1})$
\end_inset
on lines 5-8 as an instance of
on lines 5-7 as an instance of
\series bold
\emph on
PriorFactor<T>
@ -773,7 +770,7 @@ Pose2,
noiseModel::Diagonal
\series default
\emph default
by specifying three standard deviations in line 7, respectively 30 cm.
by specifying three standard deviations in line 6, respectively 30 cm.
\begin_inset space ~
\end_inset
@ -795,7 +792,7 @@ Similarly, odometry measurements are specified as
Pose2
\series default
\emph default
on line 11, with a slightly different noise model defined on line 12-13.
on line 10, with a slightly different noise model defined on line 11.
We then add the two factors
\begin_inset Formula $f_{1}(x_{1},x_{2};o_{1})$
\end_inset
@ -804,7 +801,7 @@ Pose2
\begin_inset Formula $f_{2}(x_{2},x_{3};o_{2})$
\end_inset
on lines 14-15, as instances of yet another templated class,
on lines 12-13, as instances of yet another templated class,
\series bold
\emph on
BetweenFactor<T>
@ -875,7 +872,7 @@ smoothing and mapping
.
Later in this document we will talk about how we can also use GTSAM to
do filtering (which you often do
do filtering (which often you do
\emph on
not
\emph default
@ -928,7 +925,11 @@ Values
\begin_layout Standard
The latter point is often a point of confusion with beginning users of GTSAM.
It helps to remember that when designing GTSAM we took a functional approach
of classes corresponding to mathematical objects, which are usually immutable.
of classes corresponding to mathematical objects, which are usually
\emph on
immutable
\emph default
.
You should think of a factor graph as a
\emph on
function
@ -1027,7 +1028,7 @@ NonlinearFactorGraph
\end_layout
\begin_layout Standard
The relevant output from running the example is as follows:
The relevant output from running the example is as follows:
\family typewriter
\size small
@ -1363,14 +1364,18 @@ where
\end_inset
is the measurement,
\begin_inset Formula $q$
\begin_inset Formula $q\in SE(2)$
\end_inset
is the unknown variable,
\begin_inset Formula $h(q)$
\end_inset
is a (possibly nonlinear) measurement function, and
is a
\series bold
measurement function
\series default
, and
\begin_inset Formula $\Sigma$
\end_inset
@ -1546,7 +1551,7 @@ E(q)\define h(q)-m
\end_inset
which is done on line 12.
which is done on line 14.
Importantly, because we want to use this factor for nonlinear optimization
(see e.g.,
\begin_inset CommandInset citation
@ -1599,11 +1604,11 @@ q_{y}
\begin_inset Formula $q=\left(q_{x},q_{y},q_{\theta}\right)$
\end_inset
, yields the following simple
, yields the following
\begin_inset Formula $2\times3$
\end_inset
matrix in tangent space which is the same the as the rotation matrix:
matrix:
\end_layout
\begin_layout Standard
@ -1618,6 +1623,171 @@ H=\left[\begin{array}{ccc}
\end_inset
\end_layout
\begin_layout Paragraph*
Important Note
\end_layout
\begin_layout Standard
Many of our users, when attempting to create a custom factor, are initially
surprised at the Jacobian matrix not agreeing with their intuition.
For example, above you might simply expect a
\begin_inset Formula $2\times3$
\end_inset
identity matrix.
This
\emph on
would
\emph default
be true for variables belonging to a vector space.
However, in GTSAM we define the Jacobian more generally to be the matrix
\begin_inset Formula $H$
\end_inset
such that
\begin_inset Formula
\[
h(q\exp\hat{\xi})\approx h(q)+H\xi
\]
\end_inset
where
\begin_inset Formula $\xi=(\delta x,\delta y,\delta\theta)$
\end_inset
is an incremental update and
\begin_inset Formula $\exp\hat{\xi}$
\end_inset
is the
\series bold
exponential map
\series default
for the variable we want to update.
In this case
\begin_inset Formula $q\in SE(2)$
\end_inset
, where
\begin_inset Formula $SE(2)$
\end_inset
is the group of 2D rigid transforms, implemented by
\series bold
\emph on
Pose2
\emph default
.
\series default
The exponential map for
\begin_inset Formula $SE(2)$
\end_inset
can be approximated to first order as
\begin_inset Formula
\[
\exp\hat{\xi}\approx\left[\begin{array}{ccc}
1 & -\delta\theta & \delta x\\
\delta\theta & 1 & \delta y\\
0 & 0 & 1
\end{array}\right]
\]
\end_inset
when using the
\begin_inset Formula $3\times3$
\end_inset
matrix representation for 2D poses, and hence
\begin_inset Formula
\[
h(qe^{\hat{\xi}})\approx h\left(\left[\begin{array}{ccc}
\cos(q_{\theta}) & -\sin(q_{\theta}) & q_{x}\\
\sin(q_{\theta}) & \cos(q_{\theta}) & q_{y}\\
0 & 0 & 1
\end{array}\right]\left[\begin{array}{ccc}
1 & -\delta\theta & \delta x\\
\delta\theta & 1 & \delta y\\
0 & 0 & 1
\end{array}\right]\right)=\left[\begin{array}{c}
q_{x}+\cos(q_{\theta})\delta x-\sin(q_{\theta})\delta y\\
q_{y}+\sin(q_{\theta})\delta x+\cos(q_{\theta})\delta y
\end{array}\right]
\]
\end_inset
which then explains the Jacobian
\begin_inset Formula $H$
\end_inset
.
\end_layout
\begin_layout Standard
Lie groups are very relevant in the robotics context, and you can read more
here:
\end_layout
\begin_layout Itemize
\begin_inset Flex URL
status open
\begin_layout Plain Layout
https://github.com/borglab/gtsam/blob/develop/doc/LieGroups.pdf
\end_layout
\end_inset
\end_layout
\begin_layout Itemize
\begin_inset Flex URL
status open
\begin_layout Plain Layout
https://github.com/borglab/gtsam/blob/develop/doc/math.pdf
\end_layout
\end_inset
\end_layout
\begin_layout Standard
In some cases you want to go even beyond Lie groups to a looser concept,
\series bold
manifolds
\series default
, because not all unknown variables behave like a group, e.g., the space of
3D planes, 2D lines, directions in space, etc.
For manifolds we do not always have an exponential map, but we have a retractio
n that plays the same role.
Some of this is explained here:
\end_layout
\begin_layout Itemize
\begin_inset Flex URL
status open
\begin_layout Plain Layout
https://gtsam.org/notes/GTSAM-Concepts.html
\end_layout
\end_inset
\end_layout
\begin_layout Subsection
@ -1680,13 +1850,13 @@ UnaryFactor
\series default
\emph default
instances, and add them to graph.
GTSAM uses shared pointers to refer to factors in factor graphs, and
GTSAM uses shared pointers to refer to factors, and
\series bold
\emph on
boost::make_shared
emplace_shared
\series default
\emph default
is a convenience function to simultaneously construct a class and create
is a convenience method to simultaneously construct a class and create
a
\series bold
\emph on
@ -1694,22 +1864,6 @@ shared_ptr
\series default
\emph default
to it.
\begin_inset Note Note
status collapsed
\begin_layout Plain Layout
and on lines 4-6 we add three newly created
\series bold
\emph on
UnaryFactor
\series default
\emph default
instances to the graph.
\end_layout
\end_inset
We obtain the factor graph from Figure
\begin_inset CommandInset ref
LatexCommand vref

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doc/gtsam.pdf
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2
examples/Data/randomGrid3D.xml
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@ -7,7 +7,7 @@
<count>32</count>
<item_version>1</item_version>
<item class_id="3" tracking_level="0" version="1">
<px class_id="4" class_name="JacobianFactor" tracking_level="1" version="0" object_id="_0">
<px class_id="4" class_name="gtsam::JacobianFactor" tracking_level="1" version="0" object_id="_0">
<Base class_id="5" tracking_level="0" version="0">
<Base class_id="6" tracking_level="0" version="0">
<keys_>

2
examples/Data/toy3D.xml
View File

@ -7,7 +7,7 @@
<count>2</count>
<item_version>1</item_version>
<item class_id="3" tracking_level="0" version="1">
<px class_id="4" class_name="JacobianFactor" tracking_level="1" version="0" object_id="_0">
<px class_id="4" class_name="gtsam::JacobianFactor" tracking_level="1" version="0" object_id="_0">
<Base class_id="5" tracking_level="0" version="0">
<Base class_id="6" tracking_level="0" version="0">
<keys_>

195
examples/RangeISAMExample_plaza2.cpp
View File

@ -10,62 +10,81 @@
* -------------------------------------------------------------------------- */
/**
* @file RangeISAMExample_plaza1.cpp
* @file RangeISAMExample_plaza2.cpp
* @brief A 2D Range SLAM example
* @date June 20, 2013
* @author FRank Dellaert
* @author Frank Dellaert
*/
// Both relative poses and recovered trajectory poses will be stored as Pose2 objects
// Both relative poses and recovered trajectory poses will be stored as Pose2.
#include <gtsam/geometry/Pose2.h>
using gtsam::Pose2;
// Each variable in the system (poses and landmarks) must be identified with a unique key.
// We can either use simple integer keys (1, 2, 3, ...) or symbols (X1, X2, L1).
// Here we will use Symbols
// gtsam::Vectors are dynamic Eigen vectors, Vector3 is statically sized.
#include <gtsam/base/Vector.h>
using gtsam::Vector;
using gtsam::Vector3;
// Unknown landmarks are of type Point2 (which is just a 2D Eigen vector).
#include <gtsam/geometry/Point2.h>
using gtsam::Point2;
// Each variable in the system (poses and landmarks) must be identified with a
// unique key. We can either use simple integer keys (1, 2, 3, ...) or symbols
// (X1, X2, L1). Here we will use Symbols.
#include <gtsam/inference/Symbol.h>
using gtsam::Symbol;
// We want to use iSAM2 to solve the range-SLAM problem incrementally
// We want to use iSAM2 to solve the range-SLAM problem incrementally.
#include <gtsam/nonlinear/ISAM2.h>
// iSAM2 requires as input a set set of new factors to be added stored in a factor graph,
// and initial guesses for any new variables used in the added factors
// iSAM2 requires as input a set set of new factors to be added stored in a
// factor graph, and initial guesses for any new variables in the added factors.
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/Values.h>
// We will use a non-liear solver to batch-inituialize from the first 150 frames
// We will use a non-linear solver to batch-initialize from the first 150 frames
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
// In GTSAM, measurement functions are represented as 'factors'. Several common factors
// have been provided with the library for solving robotics SLAM problems.
#include <gtsam/slam/BetweenFactor.h>
// Measurement functions are represented as 'factors'. Several common factors
// have been provided with the library for solving robotics SLAM problems:
#include <gtsam/sam/RangeFactor.h>
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/slam/dataset.h>
// Standard headers, added last, so we know headers above work on their own
// Timing, with functions below, provides nice facilities to benchmark.
#include <gtsam/base/timing.h>
using gtsam::tictoc_print_;
// Standard headers, added last, so we know headers above work on their own.
#include <fstream>
#include <iostream>
#include <random>
#include <set>
#include <string>
#include <utility>
#include <vector>
using namespace std;
using namespace gtsam;
namespace NM = gtsam::noiseModel;
// data available at http://www.frc.ri.cmu.edu/projects/emergencyresponse/RangeData/
// Datafile format (from http://www.frc.ri.cmu.edu/projects/emergencyresponse/RangeData/log.html)
// Data is second UWB ranging dataset, B2 or "plaza 2", from
// "Navigating with Ranging Radios: Five Data Sets with Ground Truth"
// by Joseph Djugash, Bradley Hamner, and Stephan Roth
// https://www.ri.cmu.edu/pub_files/2009/9/Final_5datasetsRangingRadios.pdf
// load the odometry
// DR: Odometry Input (delta distance traveled and delta heading change)
// Time (sec) Delta Dist. Trav. (m) Delta Heading (rad)
typedef pair<double, Pose2> TimedOdometry;
list<TimedOdometry> readOdometry() {
list<TimedOdometry> odometryList;
string data_file = findExampleDataFile("Plaza2_DR.txt");
ifstream is(data_file.c_str());
// Time (sec) Delta Distance Traveled (m) Delta Heading (rad)
using TimedOdometry = std::pair<double, Pose2>;
std::list<TimedOdometry> readOdometry() {
std::list<TimedOdometry> odometryList;
std::string data_file = gtsam::findExampleDataFile("Plaza2_DR.txt");
std::ifstream is(data_file.c_str());
while (is) {
double t, distance_traveled, delta_heading;
is >> t >> distance_traveled >> delta_heading;
odometryList.push_back(
TimedOdometry(t, Pose2(distance_traveled, 0, delta_heading)));
odometryList.emplace_back(t, Pose2(distance_traveled, 0, delta_heading));
}
is.clear(); /* clears the end-of-file and error flags */
return odometryList;
@ -73,90 +92,85 @@ list<TimedOdometry> readOdometry() {
// load the ranges from TD
// Time (sec) Sender / Antenna ID Receiver Node ID Range (m)
typedef boost::tuple<double, size_t, double> RangeTriple;
vector<RangeTriple> readTriples() {
vector<RangeTriple> triples;
string data_file = findExampleDataFile("Plaza2_TD.txt");
ifstream is(data_file.c_str());
using RangeTriple = boost::tuple<double, size_t, double>;
std::vector<RangeTriple> readTriples() {
std::vector<RangeTriple> triples;
std::string data_file = gtsam::findExampleDataFile("Plaza2_TD.txt");
std::ifstream is(data_file.c_str());
while (is) {
double t, sender, range;
size_t receiver;
double t, range, sender, receiver;
is >> t >> sender >> receiver >> range;
triples.push_back(RangeTriple(t, receiver, range));
triples.emplace_back(t, receiver, range);
}
is.clear(); /* clears the end-of-file and error flags */
return triples;
}
// main
int main (int argc, char** argv) {
int main(int argc, char** argv) {
// load Plaza2 data
list<TimedOdometry> odometry = readOdometry();
// size_t M = odometry.size();
std::list<TimedOdometry> odometry = readOdometry();
size_t M = odometry.size();
std::cout << "Read " << M << " odometry entries." << std::endl;
vector<RangeTriple> triples = readTriples();
std::vector<RangeTriple> triples = readTriples();
size_t K = triples.size();
std::cout << "Read " << K << " range triples." << std::endl;
// parameters
size_t minK = 150; // minimum number of range measurements to process initially
size_t incK = 25; // minimum number of range measurements to process after
bool groundTruth = false;
size_t minK =
150; // minimum number of range measurements to process initially
size_t incK = 25; // minimum number of range measurements to process after
bool robust = true;
// Set Noise parameters
Vector priorSigmas = Vector3(1,1,M_PI);
Vector priorSigmas = Vector3(1, 1, M_PI);
Vector odoSigmas = Vector3(0.05, 0.01, 0.1);
double sigmaR = 100; // range standard deviation
const NM::Base::shared_ptr // all same type
priorNoise = NM::Diagonal::Sigmas(priorSigmas), //prior
odoNoise = NM::Diagonal::Sigmas(odoSigmas), // odometry
gaussian = NM::Isotropic::Sigma(1, sigmaR), // non-robust
tukey = NM::Robust::Create(NM::mEstimator::Tukey::Create(15), gaussian), //robust
rangeNoise = robust ? tukey : gaussian;
double sigmaR = 100; // range standard deviation
const NM::Base::shared_ptr // all same type
priorNoise = NM::Diagonal::Sigmas(priorSigmas), // prior
looseNoise = NM::Isotropic::Sigma(2, 1000), // loose LM prior
odoNoise = NM::Diagonal::Sigmas(odoSigmas), // odometry
gaussian = NM::Isotropic::Sigma(1, sigmaR), // non-robust
tukey = NM::Robust::Create(NM::mEstimator::Tukey::Create(15),
gaussian), // robust
rangeNoise = robust ? tukey : gaussian;
// Initialize iSAM
ISAM2 isam;
gtsam::ISAM2 isam;
// Add prior on first pose
Pose2 pose0 = Pose2(-34.2086489999201, 45.3007639991120,
M_PI - 2.02108900000000);
NonlinearFactorGraph newFactors;
Pose2 pose0 = Pose2(-34.2086489999201, 45.3007639991120, M_PI - 2.021089);
gtsam::NonlinearFactorGraph newFactors;
newFactors.addPrior(0, pose0, priorNoise);
Values initial;
gtsam::Values initial;
initial.insert(0, pose0);
// initialize points
if (groundTruth) { // from TL file
initial.insert(symbol('L', 1), Point2(-68.9265, 18.3778));
initial.insert(symbol('L', 6), Point2(-37.5805, 69.2278));
initial.insert(symbol('L', 0), Point2(-33.6205, 26.9678));
initial.insert(symbol('L', 5), Point2(1.7095, -5.8122));
} else { // drawn from sigma=1 Gaussian in matlab version
initial.insert(symbol('L', 1), Point2(3.5784, 2.76944));
initial.insert(symbol('L', 6), Point2(-1.34989, 3.03492));
initial.insert(symbol('L', 0), Point2(0.725404, -0.0630549));
initial.insert(symbol('L', 5), Point2(0.714743, -0.204966));
}
// We will initialize landmarks randomly, and keep track of which landmarks we
// already added with a set.
std::mt19937_64 rng;
std::normal_distribution<double> normal(0.0, 100.0);
std::set<Symbol> initializedLandmarks;
// set some loop variables
size_t i = 1; // step counter
size_t k = 0; // range measurement counter
size_t i = 1; // step counter
size_t k = 0; // range measurement counter
bool initialized = false;
Pose2 lastPose = pose0;
size_t countK = 0;
// Loop over odometry
gttic_(iSAM);
for(const TimedOdometry& timedOdometry: odometry) {
//--------------------------------- odometry loop -----------------------------------------
for (const TimedOdometry& timedOdometry : odometry) {
//--------------------------------- odometry loop --------------------------
double t;
Pose2 odometry;
boost::tie(t, odometry) = timedOdometry;
// add odometry factor
newFactors.push_back(BetweenFactor<Pose2>(i-1, i, odometry, odoNoise));
newFactors.emplace_shared<gtsam::BetweenFactor<Pose2>>(i - 1, i, odometry,
odoNoise);
// predict pose and add as initial estimate
Pose2 predictedPose = lastPose.compose(odometry);
@ -166,17 +180,30 @@ int main (int argc, char** argv) {
// Check if there are range factors to be added
while (k < K && t >= boost::get<0>(triples[k])) {
size_t j = boost::get<1>(triples[k]);
Symbol landmark_key('L', j);
double range = boost::get<2>(triples[k]);
newFactors.push_back(RangeFactor<Pose2, Point2>(i, symbol('L', j), range,rangeNoise));
newFactors.emplace_shared<gtsam::RangeFactor<Pose2, Point2>>(
i, landmark_key, range, rangeNoise);
if (initializedLandmarks.count(landmark_key) == 0) {
std::cout << "adding landmark " << j << std::endl;
double x = normal(rng), y = normal(rng);
initial.insert(landmark_key, Point2(x, y));
initializedLandmarks.insert(landmark_key);
// We also add a very loose prior on the landmark in case there is only
// one sighting, which cannot fully determine the landmark.
newFactors.emplace_shared<gtsam::PriorFactor<Point2>>(
landmark_key, Point2(0, 0), looseNoise);
}
k = k + 1;
countK = countK + 1;
}
// Check whether to update iSAM 2
if ((k > minK) && (countK > incK)) {
if (!initialized) { // Do a full optimize for first minK ranges
if (!initialized) { // Do a full optimize for first minK ranges
std::cout << "Initializing at time " << k << std::endl;
gttic_(batchInitialization);
LevenbergMarquardtOptimizer batchOptimizer(newFactors, initial);
gtsam::LevenbergMarquardtOptimizer batchOptimizer(newFactors, initial);
initial = batchOptimizer.optimize();
gttoc_(batchInitialization);
initialized = true;
@ -185,21 +212,27 @@ int main (int argc, char** argv) {
isam.update(newFactors, initial);
gttoc_(update);
gttic_(calculateEstimate);
Values result = isam.calculateEstimate();
gtsam::Values result = isam.calculateEstimate();
gttoc_(calculateEstimate);
lastPose = result.at<Pose2>(i);
newFactors = NonlinearFactorGraph();
initial = Values();
newFactors = gtsam::NonlinearFactorGraph();
initial = gtsam::Values();
countK = 0;
}
i += 1;
//--------------------------------- odometry loop -----------------------------------------
} // end for
//--------------------------------- odometry loop --------------------------
} // end for
gttoc_(iSAM);
// Print timings
tictoc_print_();
// Print optimized landmarks:
gtsam::Values finalResult = isam.calculateEstimate();
for (auto&& landmark_key : initializedLandmarks) {
Point2 p = finalResult.at<Point2>(landmark_key);
std::cout << landmark_key << ":" << p.transpose() << "\n";
}
exit(0);
}

12
examples/SFMExampleExpressions_bal.cpp
View File

@ -26,9 +26,12 @@
#include <gtsam/nonlinear/ExpressionFactorGraph.h>
// Header order is close to far
#include <gtsam/inference/Symbol.h>
#include <gtsam/sfm/SfmData.h> // for loading BAL datasets !
#include <gtsam/slam/dataset.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/slam/dataset.h> // for loading BAL datasets !
#include <gtsam/inference/Symbol.h>
#include <boost/format.hpp>
#include <vector>
using namespace std;
@ -46,10 +49,9 @@ int main(int argc, char* argv[]) {
if (argc > 1) filename = string(argv[1]);
// Load the SfM data from file
SfmData mydata;
readBAL(filename, mydata);
SfmData mydata = SfmData::FromBalFile(filename);
cout << boost::format("read %1% tracks on %2% cameras\n") %
mydata.number_tracks() % mydata.number_cameras();
mydata.numberTracks() % mydata.numberCameras();
// Create a factor graph
ExpressionFactorGraph graph;

16
examples/SFMExample_bal.cpp
View File

@ -10,17 +10,20 @@
* -------------------------------------------------------------------------- */
/**
* @file SFMExample.cpp
* @file SFMExample_bal.cpp
* @brief Solve a structure-from-motion problem from a "Bundle Adjustment in the Large" file
* @author Frank Dellaert
*/
// For an explanation of headers, see SFMExample.cpp
#include <gtsam/inference/Symbol.h>
#include <gtsam/sfm/SfmData.h> // for loading BAL datasets !
#include <gtsam/slam/GeneralSFMFactor.h>
#include <gtsam/slam/dataset.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/slam/GeneralSFMFactor.h>
#include <gtsam/slam/dataset.h> // for loading BAL datasets !
#include <gtsam/inference/Symbol.h>
#include <boost/format.hpp>
#include <vector>
using namespace std;
@ -41,9 +44,8 @@ int main (int argc, char* argv[]) {
if (argc>1) filename = string(argv[1]);
// Load the SfM data from file
SfmData mydata;
readBAL(filename, mydata);
cout << boost::format("read %1% tracks on %2% cameras\n") % mydata.number_tracks() % mydata.number_cameras();
SfmData mydata = SfmData::FromBalFile(filename);
cout << boost::format("read %1% tracks on %2% cameras\n") % mydata.numberTracks() % mydata.numberCameras();
// Create a factor graph
NonlinearFactorGraph graph;

18
examples/SFMExample_bal_COLAMD_METIS.cpp
View File

@ -17,15 +17,16 @@
*/
// For an explanation of headers, see SFMExample.cpp
#include <gtsam/inference/Symbol.h>
#include <gtsam/inference/Ordering.h>
#include <gtsam/slam/GeneralSFMFactor.h>
#include <gtsam/sfm/SfmData.h> // for loading BAL datasets !
#include <gtsam/slam/dataset.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/slam/GeneralSFMFactor.h>
#include <gtsam/slam/dataset.h> // for loading BAL datasets !
#include <gtsam/inference/Symbol.h>
#include <gtsam/inference/Ordering.h>
#include <gtsam/base/timing.h>
#include <boost/format.hpp>
#include <vector>
using namespace std;
@ -45,10 +46,9 @@ int main(int argc, char* argv[]) {
if (argc > 1) filename = string(argv[1]);
// Load the SfM data from file
SfmData mydata;
readBAL(filename, mydata);
SfmData mydata = SfmData::FromBalFile(filename);
cout << boost::format("read %1% tracks on %2% cameras\n") %
mydata.number_tracks() % mydata.number_cameras();
mydata.numberTracks() % mydata.numberCameras();
// Create a factor graph
NonlinearFactorGraph graph;
@ -131,7 +131,7 @@ int main(int argc, char* argv[]) {
cout << "Time comparison by solving " << filename << " results:" << endl;
cout << boost::format("%1% point tracks and %2% cameras\n") %
mydata.number_tracks() % mydata.number_cameras()
mydata.numberTracks() % mydata.numberCameras()
<< endl;
tictoc_print_();

4
examples/SFMdata.h
View File

@ -22,6 +22,8 @@
* Passing function argument allows to specificy an initial position, a pose increment and step count.
*/
#pragma once
// As this is a full 3D problem, we will use Pose3 variables to represent the camera
// positions and Point3 variables (x, y, z) to represent the landmark coordinates.
// Camera observations of landmarks (i.e. pixel coordinates) will be stored as Point2 (x, y).
@ -66,4 +68,4 @@ std::vector<gtsam::Pose3> createPoses(
}
return poses;
}
}

4
gtsam/base/FastSet.h
View File

@ -18,6 +18,10 @@
#pragma once
#include <boost/version.hpp>
#if BOOST_VERSION >= 107400
#include <boost/serialization/library_version_type.hpp>
#endif
#include <boost/serialization/nvp.hpp>
#include <boost/serialization/set.hpp>
#include <gtsam/base/FastDefaultAllocator.h>

1
gtsam/base/Matrix.cpp
View File

@ -25,6 +25,7 @@
#include <boost/tuple/tuple.hpp>
#include <boost/tokenizer.hpp>
#include <boost/format.hpp>
#include <cstdarg>
#include <cstring>

87
gtsam/base/Matrix.h
View File

@ -26,12 +26,9 @@
#include <gtsam/base/OptionalJacobian.h>
#include <gtsam/base/Vector.h>
#include <gtsam/config.h>
#include <boost/format.hpp>
#include <functional>
#include <boost/tuple/tuple.hpp>
#include <boost/math/special_functions/fpclassify.hpp>
#include <vector>
/**
* Matrix is a typedef in the gtsam namespace
@ -523,82 +520,4 @@ GTSAM_EXPORT Matrix LLt(const Matrix& A);
GTSAM_EXPORT Matrix RtR(const Matrix& A);
GTSAM_EXPORT Vector columnNormSquare(const Matrix &A);
} // namespace gtsam
#include <boost/serialization/nvp.hpp>
#include <boost/serialization/array.hpp>
#include <boost/serialization/split_free.hpp>
namespace boost {
namespace serialization {
/**
* Ref. https://stackoverflow.com/questions/18382457/eigen-and-boostserialize/22903063#22903063
*
* Eigen supports calling resize() on both static and dynamic matrices.
* This allows for a uniform API, with resize having no effect if the static matrix
* is already the correct size.
* https://eigen.tuxfamily.org/dox/group__TutorialMatrixClass.html#TutorialMatrixSizesResizing
*
* We use all the Matrix template parameters to ensure wide compatibility.
*
* eigen_typekit in ROS uses the same code
* http://docs.ros.org/lunar/api/eigen_typekit/html/eigen__mqueue_8cpp_source.html
*/
// split version - sends sizes ahead
template<class Archive,
typename Scalar_,
int Rows_,
int Cols_,
int Ops_,
int MaxRows_,
int MaxCols_>
void save(Archive & ar,
const Eigen::Matrix<Scalar_, Rows_, Cols_, Ops_, MaxRows_, MaxCols_> & m,
const unsigned int /*version*/) {
const size_t rows = m.rows(), cols = m.cols();
ar << BOOST_SERIALIZATION_NVP(rows);
ar << BOOST_SERIALIZATION_NVP(cols);
ar << make_nvp("data", make_array(m.data(), m.size()));
}
template<class Archive,
typename Scalar_,
int Rows_,
int Cols_,
int Ops_,
int MaxRows_,
int MaxCols_>
void load(Archive & ar,
Eigen::Matrix<Scalar_, Rows_, Cols_, Ops_, MaxRows_, MaxCols_> & m,
const unsigned int /*version*/) {
size_t rows, cols;
ar >> BOOST_SERIALIZATION_NVP(rows);
ar >> BOOST_SERIALIZATION_NVP(cols);
m.resize(rows, cols);
ar >> make_nvp("data", make_array(m.data(), m.size()));
}
// templated version of BOOST_SERIALIZATION_SPLIT_FREE(Eigen::Matrix);
template<class Archive,
typename Scalar_,
int Rows_,
int Cols_,
int Ops_,
int MaxRows_,
int MaxCols_>
void serialize(Archive & ar,
Eigen::Matrix<Scalar_, Rows_, Cols_, Ops_, MaxRows_, MaxCols_> & m,
const unsigned int version) {
split_free(ar, m, version);
}
// specialized to Matrix for MATLAB wrapper
template <class Archive>
void serialize(Archive& ar, gtsam::Matrix& m, const unsigned int version) {
split_free(ar, m, version);
}
} // namespace serialization
} // namespace boost
} // namespace gtsam

89
gtsam/base/MatrixSerialization.h Normal file
View File

@ -0,0 +1,89 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file MatrixSerialization.h
* @brief Serialization for matrices
* @author Frank Dellaert
* @date February 2022
*/
// \callgraph
#pragma once
#include <gtsam/base/Matrix.h>
#include <boost/serialization/array.hpp>
#include <boost/serialization/nvp.hpp>
#include <boost/serialization/split_free.hpp>
namespace boost {
namespace serialization {
/**
* Ref.
* https://stackoverflow.com/questions/18382457/eigen-and-boostserialize/22903063#22903063
*
* Eigen supports calling resize() on both static and dynamic matrices.
* This allows for a uniform API, with resize having no effect if the static
* matrix is already the correct size.
* https://eigen.tuxfamily.org/dox/group__TutorialMatrixClass.html#TutorialMatrixSizesResizing
*
* We use all the Matrix template parameters to ensure wide compatibility.
*
* eigen_typekit in ROS uses the same code
* http://docs.ros.org/lunar/api/eigen_typekit/html/eigen__mqueue_8cpp_source.html
*/
// split version - sends sizes ahead
template <class Archive, typename Scalar_, int Rows_, int Cols_, int Ops_,
int MaxRows_, int MaxCols_>
void save(
Archive& ar,
const Eigen::Matrix<Scalar_, Rows_, Cols_, Ops_, MaxRows_, MaxCols_>& m,
const unsigned int /*version*/) {
const size_t rows = m.rows(), cols = m.cols();
ar << BOOST_SERIALIZATION_NVP(rows);
ar << BOOST_SERIALIZATION_NVP(cols);
ar << make_nvp("data", make_array(m.data(), m.size()));
}
template <class Archive, typename Scalar_, int Rows_, int Cols_, int Ops_,
int MaxRows_, int MaxCols_>
void load(Archive& ar,
Eigen::Matrix<Scalar_, Rows_, Cols_, Ops_, MaxRows_, MaxCols_>& m,
const unsigned int /*version*/) {
size_t rows, cols;
ar >> BOOST_SERIALIZATION_NVP(rows);
ar >> BOOST_SERIALIZATION_NVP(cols);
m.resize(rows, cols);
ar >> make_nvp("data", make_array(m.data(), m.size()));
}
// templated version of BOOST_SERIALIZATION_SPLIT_FREE(Eigen::Matrix);
template <class Archive, typename Scalar_, int Rows_, int Cols_, int Ops_,
int MaxRows_, int MaxCols_>
void serialize(
Archive& ar,
Eigen::Matrix<Scalar_, Rows_, Cols_, Ops_, MaxRows_, MaxCols_>& m,
const unsigned int version) {
split_free(ar, m, version);
}
// specialized to Matrix for MATLAB wrapper
template <class Archive>
void serialize(Archive& ar, gtsam::Matrix& m, const unsigned int version) {
split_free(ar, m, version);
}
} // namespace serialization
} // namespace boost

20
gtsam/base/OptionalJacobian.h
View File

@ -20,6 +20,8 @@
#pragma once
#include <gtsam/config.h> // Configuration from CMake
#include <Eigen/Dense>
#include <stdexcept>
#include <string>
#ifndef OPTIONALJACOBIAN_NOBOOST
#include <boost/optional.hpp>
@ -96,6 +98,24 @@ public:
usurp(dynamic->data());
}
/**
* @brief Constructor from an Eigen::Ref *value*. Will not usurp if dimension is wrong
* @note This is important so we don't overwrite someone else's memory!
*/
template<class MATRIX>
OptionalJacobian(Eigen::Ref<MATRIX> dynamic_ref) :
map_(nullptr) {
if (dynamic_ref.rows() == Rows && dynamic_ref.cols() == Cols && !dynamic_ref.IsRowMajor) {
usurp(dynamic_ref.data());
} else {
throw std::invalid_argument(
std::string("OptionalJacobian called with wrong dimensions or "
"storage order.\n"
"Expected: ") +
"(" + std::to_string(Rows) + ", " + std::to_string(Cols) + ")");
}
}
#ifndef OPTIONALJACOBIAN_NOBOOST
/// Constructor with boost::none just makes empty

1
gtsam/base/Value.h
View File

@ -21,6 +21,7 @@
#include <gtsam/config.h> // Configuration from CMake
#include <gtsam/base/Vector.h>
#include <boost/serialization/nvp.hpp>
#include <boost/serialization/assume_abstract.hpp>
#include <memory>

44
gtsam/base/Vector.h
View File

@ -264,46 +264,4 @@ GTSAM_EXPORT Vector concatVectors(const std::list<Vector>& vs);
* concatenate Vectors
*/
GTSAM_EXPORT Vector concatVectors(size_t nrVectors, ...);
} // namespace gtsam
#include <boost/serialization/nvp.hpp>
#include <boost/serialization/array.hpp>
#include <boost/serialization/split_free.hpp>
namespace boost {
namespace serialization {
// split version - copies into an STL vector for serialization
template<class Archive>
void save(Archive & ar, const gtsam::Vector & v, unsigned int /*version*/) {
const size_t size = v.size();
ar << BOOST_SERIALIZATION_NVP(size);
ar << make_nvp("data", make_array(v.data(), v.size()));
}
template<class Archive>
void load(Archive & ar, gtsam::Vector & v, unsigned int /*version*/) {
size_t size;
ar >> BOOST_SERIALIZATION_NVP(size);
v.resize(size);
ar >> make_nvp("data", make_array(v.data(), v.size()));
}
// split version - copies into an STL vector for serialization
template<class Archive, int D>
void save(Archive & ar, const Eigen::Matrix<double,D,1> & v, unsigned int /*version*/) {
ar << make_nvp("data", make_array(v.data(), v.RowsAtCompileTime));
}
template<class Archive, int D>
void load(Archive & ar, Eigen::Matrix<double,D,1> & v, unsigned int /*version*/) {
ar >> make_nvp("data", make_array(v.data(), v.RowsAtCompileTime));
}
} // namespace serialization
} // namespace boost
BOOST_SERIALIZATION_SPLIT_FREE(gtsam::Vector)
BOOST_SERIALIZATION_SPLIT_FREE(gtsam::Vector2)
BOOST_SERIALIZATION_SPLIT_FREE(gtsam::Vector3)
BOOST_SERIALIZATION_SPLIT_FREE(gtsam::Vector6)
} // namespace gtsam

65
gtsam/base/VectorSerialization.h Normal file
View File

@ -0,0 +1,65 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file VectorSerialization.h
* @brief serialization for Vectors
* @author Frank Dellaert
* @date February 2022
*/
#pragma once
#include <gtsam/base/Vector.h>
#include <boost/serialization/array.hpp>
#include <boost/serialization/nvp.hpp>
#include <boost/serialization/split_free.hpp>
namespace boost {
namespace serialization {
// split version - copies into an STL vector for serialization
template <class Archive>
void save(Archive& ar, const gtsam::Vector& v, unsigned int /*version*/) {
const size_t size = v.size();
ar << BOOST_SERIALIZATION_NVP(size);
ar << make_nvp("data", make_array(v.data(), v.size()));
}
template <class Archive>
void load(Archive& ar, gtsam::Vector& v, unsigned int /*version*/) {
size_t size;
ar >> BOOST_SERIALIZATION_NVP(size);
v.resize(size);
ar >> make_nvp("data", make_array(v.data(), v.size()));
}
// split version - copies into an STL vector for serialization
template <class Archive, int D>
void save(Archive& ar, const Eigen::Matrix<double, D, 1>& v,
unsigned int /*version*/) {
ar << make_nvp("data", make_array(v.data(), v.RowsAtCompileTime));
}
template <class Archive, int D>
void load(Archive& ar, Eigen::Matrix<double, D, 1>& v,
unsigned int /*version*/) {
ar >> make_nvp("data", make_array(v.data(), v.RowsAtCompileTime));
}
} // namespace serialization
} // namespace boost
BOOST_SERIALIZATION_SPLIT_FREE(gtsam::Vector)
BOOST_SERIALIZATION_SPLIT_FREE(gtsam::Vector2)
BOOST_SERIALIZATION_SPLIT_FREE(gtsam::Vector3)
BOOST_SERIALIZATION_SPLIT_FREE(gtsam::Vector6)

1
gtsam/base/VerticalBlockMatrix.h
View File

@ -18,6 +18,7 @@
#pragma once
#include <gtsam/base/Matrix.h>
#include <gtsam/base/MatrixSerialization.h>
#include <gtsam/base/FastVector.h>
namespace gtsam {

1
gtsam/base/base.i
View File

@ -82,6 +82,7 @@ class IndexPairSetMap {
};
#include <gtsam/base/Matrix.h>
#include <gtsam/base/MatrixSerialization.h>
bool linear_independent(Matrix A, Matrix B, double tol);
#include <gtsam/base/Value.h>

1
gtsam/base/cholesky.h
View File

@ -18,7 +18,6 @@
#pragma once
#include <gtsam/base/Matrix.h>
#include <boost/shared_ptr.hpp>
namespace gtsam {

1
gtsam/base/serialization.h
View File

@ -25,6 +25,7 @@
#include <string>
// includes for standard serialization types
#include <boost/serialization/version.hpp>
#include <boost/serialization/optional.hpp>
#include <boost/serialization/shared_ptr.hpp>
#include <boost/serialization/vector.hpp>

2
gtsam/base/serializationTestHelpers.h
View File

@ -42,7 +42,7 @@ T create() {
}
// Creates or empties a folder in the build folder and returns the relative path
boost::filesystem::path resetFilesystem(
inline boost::filesystem::path resetFilesystem(
boost::filesystem::path folder = "actual") {
boost::filesystem::remove_all(folder);
boost::filesystem::create_directory(folder);

1
gtsam/base/tests/testSerializationBase.cpp
View File

@ -19,6 +19,7 @@
#include <gtsam/inference/Key.h>
#include <gtsam/base/Matrix.h>
#include <gtsam/base/MatrixSerialization.h>
#include <gtsam/base/Vector.h>
#include <gtsam/base/FastList.h>
#include <gtsam/base/FastMap.h>

7
gtsam/basis/Basis.h
View File

@ -92,7 +92,7 @@ Matrix kroneckerProductIdentity(const Weights& w) {
/// CRTP Base class for function bases
template <typename DERIVED>
class GTSAM_EXPORT Basis {
class Basis {
public:
/**
* Calculate weights for all x in vector X.
@ -497,11 +497,6 @@ class GTSAM_EXPORT Basis {
}
};
// Vector version for MATLAB :-(
static double Derivative(double x, const Vector& p, //
OptionalJacobian</*1xN*/ -1, -1> H = boost::none) {
return DerivativeFunctor(x)(p.transpose(), H);
}
};
} // namespace gtsam

21
gtsam/basis/BasisFactors.h
View File

@ -29,9 +29,12 @@ namespace gtsam {
* pseudo-spectral parameterization.
*
* @tparam BASIS The basis class to use e.g. Chebyshev2
*
* Example, degree 8 Chebyshev polynomial measured at x=0.5:
* EvaluationFactor<Chebyshev2> factor(key, measured, model, 8, 0.5);
*/
template <class BASIS>
class GTSAM_EXPORT EvaluationFactor : public FunctorizedFactor<double, Vector> {
class EvaluationFactor : public FunctorizedFactor<double, Vector> {
private:
using Base = FunctorizedFactor<double, Vector>;
@ -47,7 +50,7 @@ class GTSAM_EXPORT EvaluationFactor : public FunctorizedFactor<double, Vector> {
* @param N The degree of the polynomial.
* @param x The point at which to evaluate the polynomial.
*/
EvaluationFactor(Key key, const double &z, const SharedNoiseModel &model,
EvaluationFactor(Key key, double z, const SharedNoiseModel &model,
const size_t N, double x)
: Base(key, z, model, typename BASIS::EvaluationFunctor(N, x)) {}
@ -62,7 +65,7 @@ class GTSAM_EXPORT EvaluationFactor : public FunctorizedFactor<double, Vector> {
* @param a Lower bound for the polynomial.
* @param b Upper bound for the polynomial.
*/
EvaluationFactor(Key key, const double &z, const SharedNoiseModel &model,
EvaluationFactor(Key key, double z, const SharedNoiseModel &model,
const size_t N, double x, double a, double b)
: Base(key, z, model, typename BASIS::EvaluationFunctor(N, x, a, b)) {}
@ -85,7 +88,7 @@ class GTSAM_EXPORT EvaluationFactor : public FunctorizedFactor<double, Vector> {
* @param M: Size of the evaluated state vector.
*/
template <class BASIS, int M>
class GTSAM_EXPORT VectorEvaluationFactor
class VectorEvaluationFactor
: public FunctorizedFactor<Vector, ParameterMatrix<M>> {
private:
using Base = FunctorizedFactor<Vector, ParameterMatrix<M>>;
@ -148,7 +151,7 @@ class GTSAM_EXPORT VectorEvaluationFactor
* where N is the degree and i is the component index.
*/
template <class BASIS, size_t P>
class GTSAM_EXPORT VectorComponentFactor
class VectorComponentFactor
: public FunctorizedFactor<double, ParameterMatrix<P>> {
private:
using Base = FunctorizedFactor<double, ParameterMatrix<P>>;
@ -217,7 +220,7 @@ class GTSAM_EXPORT VectorComponentFactor
* where `x` is the value (e.g. timestep) at which the rotation was evaluated.
*/
template <class BASIS, typename T>
class GTSAM_EXPORT ManifoldEvaluationFactor
class ManifoldEvaluationFactor
: public FunctorizedFactor<T, ParameterMatrix<traits<T>::dimension>> {
private:
using Base = FunctorizedFactor<T, ParameterMatrix<traits<T>::dimension>>;
@ -269,7 +272,7 @@ class GTSAM_EXPORT ManifoldEvaluationFactor
* @param BASIS: The basis class to use e.g. Chebyshev2
*/
template <class BASIS>
class GTSAM_EXPORT DerivativeFactor
class DerivativeFactor
: public FunctorizedFactor<double, typename BASIS::Parameters> {
private:
using Base = FunctorizedFactor<double, typename BASIS::Parameters>;
@ -318,7 +321,7 @@ class GTSAM_EXPORT DerivativeFactor
* @param M: Size of the evaluated state vector derivative.
*/
template <class BASIS, int M>
class GTSAM_EXPORT VectorDerivativeFactor
class VectorDerivativeFactor
: public FunctorizedFactor<Vector, ParameterMatrix<M>> {
private:
using Base = FunctorizedFactor<Vector, ParameterMatrix<M>>;
@ -371,7 +374,7 @@ class GTSAM_EXPORT VectorDerivativeFactor
* @param P: Size of the control component derivative.
*/
template <class BASIS, int P>
class GTSAM_EXPORT ComponentDerivativeFactor
class ComponentDerivativeFactor
: public FunctorizedFactor<double, ParameterMatrix<P>> {
private:
using Base = FunctorizedFactor<double, ParameterMatrix<P>>;

6
gtsam/basis/Chebyshev.h
View File

@ -21,8 +21,6 @@
#include <gtsam/base/Manifold.h>
#include <gtsam/basis/Basis.h>
#include <unsupported/Eigen/KroneckerProduct>
namespace gtsam {
/**
@ -31,7 +29,7 @@ namespace gtsam {
* These are typically denoted with the symbol T_n, where n is the degree.
* The parameter N is the number of coefficients, i.e., N = n+1.
*/
struct Chebyshev1Basis : Basis<Chebyshev1Basis> {
struct GTSAM_EXPORT Chebyshev1Basis : Basis<Chebyshev1Basis> {
using Parameters = Eigen::Matrix<double, -1, 1 /*Nx1*/>;
Parameters parameters_;
@ -79,7 +77,7 @@ struct Chebyshev1Basis : Basis<Chebyshev1Basis> {
* functions. In this sense, they are like the sines and cosines of the Fourier
* basis.
*/
struct Chebyshev2Basis : Basis<Chebyshev2Basis> {
struct GTSAM_EXPORT Chebyshev2Basis : Basis<Chebyshev2Basis> {
using Parameters = Eigen::Matrix<double, -1, 1 /*Nx1*/>;
/**

3
gtsam/basis/Chebyshev2.h
View File

@ -22,8 +22,7 @@
*
* This is different from Chebyshev.h since it leverage ideas from
* pseudo-spectral optimization, i.e. we don't decompose into basis functions,
* rather estimate function parameters that enforce function nodes at Chebyshev
* points.
* rather estimate function values at the Chebyshev points.
*
* Please refer to Agrawal21icra for more details.
*

2
gtsam/basis/Fourier.h
View File

@ -24,7 +24,7 @@
namespace gtsam {
/// Fourier basis
class GTSAM_EXPORT FourierBasis : public Basis<FourierBasis> {
class FourierBasis : public Basis<FourierBasis> {
public:
using Parameters = Eigen::Matrix<double, /*Nx1*/ -1, 1>;
using DiffMatrix = Eigen::Matrix<double, /*NxN*/ -1, -1>;

3
gtsam/basis/basis.i
View File

@ -44,9 +44,6 @@ class Chebyshev2 {
static Matrix DerivativeWeights(size_t N, double x, double a, double b);
static Matrix IntegrationWeights(size_t N, double a, double b);
static Matrix DifferentiationMatrix(size_t N, double a, double b);
// TODO Needs OptionalJacobian
// static double Derivative(double x, Vector f);
};
#include <gtsam/basis/ParameterMatrix.h>

230
gtsam/basis/tests/testBasisFactors.cpp Normal file
View File

@ -0,0 +1,230 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------1-------------------------------------------
*/
/**
* @file testBasisFactors.cpp
* @date May 31, 2020
* @author Varun Agrawal
* @brief unit tests for factors in BasisFactors.h
*/
#include <gtsam/basis/Basis.h>
#include <gtsam/basis/BasisFactors.h>
#include <gtsam/basis/Chebyshev2.h>
#include <gtsam/geometry/Pose2.h>
#include <gtsam/nonlinear/FunctorizedFactor.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/nonlinear/factorTesting.h>
#include <gtsam/inference/Symbol.h>
#include <gtsam/base/Testable.h>
#include <gtsam/base/TestableAssertions.h>
#include <gtsam/base/Vector.h>
#include <CppUnitLite/TestHarness.h>
using gtsam::noiseModel::Isotropic;
using gtsam::Pose2;
using gtsam::Vector;
using gtsam::Values;
using gtsam::Chebyshev2;
using gtsam::ParameterMatrix;
using gtsam::LevenbergMarquardtParams;
using gtsam::LevenbergMarquardtOptimizer;
using gtsam::NonlinearFactorGraph;
using gtsam::NonlinearOptimizerParams;
constexpr size_t N = 2;
// Key used in all tests
const gtsam::Symbol key('X', 0);
//******************************************************************************
TEST(BasisFactors, EvaluationFactor) {
using gtsam::EvaluationFactor;
double measured = 0;
auto model = Isotropic::Sigma(1, 1.0);
EvaluationFactor<Chebyshev2> factor(key, measured, model, N, 0);
NonlinearFactorGraph graph;
graph.add(factor);
Vector functionValues(N);
functionValues.setZero();
Values initial;
initial.insert<Vector>(key, functionValues);
LevenbergMarquardtParams parameters;
parameters.setMaxIterations(20);
Values result =
LevenbergMarquardtOptimizer(graph, initial, parameters).optimize();
EXPECT_DOUBLES_EQUAL(0, graph.error(result), 1e-9);
}
//******************************************************************************
TEST(BasisFactors, VectorEvaluationFactor) {
using gtsam::VectorEvaluationFactor;
const size_t M = 4;
const Vector measured = Vector::Zero(M);
auto model = Isotropic::Sigma(M, 1.0);
VectorEvaluationFactor<Chebyshev2, M> factor(key, measured, model, N, 0);
NonlinearFactorGraph graph;
graph.add(factor);
ParameterMatrix<M> stateMatrix(N);
Values initial;
initial.insert<ParameterMatrix<M>>(key, stateMatrix);
LevenbergMarquardtParams parameters;
parameters.setMaxIterations(20);
Values result =
LevenbergMarquardtOptimizer(graph, initial, parameters).optimize();
EXPECT_DOUBLES_EQUAL(0, graph.error(result), 1e-9);
}
//******************************************************************************
TEST(BasisFactors, Print) {
using gtsam::VectorEvaluationFactor;
const size_t M = 1;
const Vector measured = Vector::Ones(M) * 42;
auto model = Isotropic::Sigma(M, 1.0);
VectorEvaluationFactor<Chebyshev2, M> factor(key, measured, model, N, 0);
std::string expected =
" keys = { X0 }\n"
" noise model: unit (1) \n"
"FunctorizedFactor(X0)\n"
" measurement: [\n"
" 42\n"
"]\n"
" noise model sigmas: 1\n";
EXPECT(assert_print_equal(expected, factor));
}
//******************************************************************************
TEST(BasisFactors, VectorComponentFactor) {
using gtsam::VectorComponentFactor;
const int P = 4;
const size_t i = 2;
const double measured = 0.0, t = 3.0, a = 2.0, b = 4.0;
auto model = Isotropic::Sigma(1, 1.0);
VectorComponentFactor<Chebyshev2, P> factor(key, measured, model, N, i,
t, a, b);
NonlinearFactorGraph graph;
graph.add(factor);
ParameterMatrix<P> stateMatrix(N);
Values initial;
initial.insert<ParameterMatrix<P>>(key, stateMatrix);
LevenbergMarquardtParams parameters;
parameters.setMaxIterations(20);
Values result =
LevenbergMarquardtOptimizer(graph, initial, parameters).optimize();
EXPECT_DOUBLES_EQUAL(0, graph.error(result), 1e-9);
}
//******************************************************************************
TEST(BasisFactors, ManifoldEvaluationFactor) {
using gtsam::ManifoldEvaluationFactor;
const Pose2 measured;
const double t = 3.0, a = 2.0, b = 4.0;
auto model = Isotropic::Sigma(3, 1.0);
ManifoldEvaluationFactor<Chebyshev2, Pose2> factor(key, measured, model, N,
t, a, b);
NonlinearFactorGraph graph;
graph.add(factor);
ParameterMatrix<3> stateMatrix(N);
Values initial;
initial.insert<ParameterMatrix<3>>(key, stateMatrix);
LevenbergMarquardtParams parameters;
parameters.setMaxIterations(20);
Values result =
LevenbergMarquardtOptimizer(graph, initial, parameters).optimize();
EXPECT_DOUBLES_EQUAL(0, graph.error(result), 1e-9);
}
//******************************************************************************
TEST(BasisFactors, VecDerivativePrior) {
using gtsam::VectorDerivativeFactor;
const size_t M = 4;
const Vector measured = Vector::Zero(M);
auto model = Isotropic::Sigma(M, 1.0);
VectorDerivativeFactor<Chebyshev2, M> vecDPrior(key, measured, model, N, 0);
NonlinearFactorGraph graph;
graph.add(vecDPrior);
ParameterMatrix<M> stateMatrix(N);
Values initial;
initial.insert<ParameterMatrix<M>>(key, stateMatrix);
LevenbergMarquardtParams parameters;
parameters.setMaxIterations(20);
Values result =
LevenbergMarquardtOptimizer(graph, initial, parameters).optimize();
EXPECT_DOUBLES_EQUAL(0, graph.error(result), 1e-9);
}
//******************************************************************************
TEST(BasisFactors, ComponentDerivativeFactor) {
using gtsam::ComponentDerivativeFactor;
const size_t M = 4;
double measured = 0;
auto model = Isotropic::Sigma(1, 1.0);
ComponentDerivativeFactor<Chebyshev2, M> controlDPrior(key, measured, model,
N, 0, 0);
NonlinearFactorGraph graph;
graph.add(controlDPrior);
Values initial;
ParameterMatrix<M> stateMatrix(N);
initial.insert<ParameterMatrix<M>>(key, stateMatrix);
LevenbergMarquardtParams parameters;
parameters.setMaxIterations(20);
Values result =
LevenbergMarquardtOptimizer(graph, initial, parameters).optimize();
EXPECT_DOUBLES_EQUAL(0, graph.error(result), 1e-9);
}
/* ************************************************************************* */
int main() {
TestResult tr;
return TestRegistry::runAllTests(tr);
}
/* ************************************************************************* */

3
gtsam/basis/tests/testChebyshev.cpp
View File

@ -25,9 +25,10 @@
using namespace std;
using namespace gtsam;
namespace {
auto model = noiseModel::Unit::Create(1);
const size_t N = 3;
} // namespace
//******************************************************************************
TEST(Chebyshev, Chebyshev1) {

38
gtsam/basis/tests/testChebyshev2.cpp
View File

@ -10,26 +10,30 @@
* -------------------------------------------------------------------------- */
/**
* @file testChebyshev.cpp
* @file testChebyshev2.cpp
* @date July 4, 2020
* @author Varun Agrawal
* @brief Unit tests for Chebyshev Basis Decompositions via pseudo-spectral
* methods
*/
#include <CppUnitLite/TestHarness.h>
#include <gtsam/base/Testable.h>
#include <gtsam/basis/Chebyshev2.h>
#include <gtsam/basis/FitBasis.h>
#include <gtsam/geometry/Pose2.h>
#include <gtsam/nonlinear/factorTesting.h>
#include <gtsam/base/Testable.h>
#include <CppUnitLite/TestHarness.h>
using namespace std;
using namespace gtsam;
using namespace boost::placeholders;
namespace {
noiseModel::Diagonal::shared_ptr model = noiseModel::Unit::Create(1);
const size_t N = 32;
} // namespace
//******************************************************************************
TEST(Chebyshev2, Point) {
@ -121,12 +125,30 @@ TEST(Chebyshev2, InterpolateVector) {
EXPECT(assert_equal(numericalH, actualH, 1e-9));
}
//******************************************************************************
// Interpolating poses using the exponential map
TEST(Chebyshev2, InterpolatePose2) {
double t = 30, a = 0, b = 100;
ParameterMatrix<3> X(N);
X.row(0) = Chebyshev2::Points(N, a, b); // slope 1 ramp
X.row(1) = Vector::Zero(N);
X.row(2) = 0.1 * Vector::Ones(N);
Vector xi(3);
xi << t, 0, 0.1;
Chebyshev2::ManifoldEvaluationFunctor<Pose2> fx(N, t, a, b);
// We use xi as canonical coordinates via exponential map
Pose2 expected = Pose2::ChartAtOrigin::Retract(xi);
EXPECT(assert_equal(expected, fx(X)));
}
//******************************************************************************
TEST(Chebyshev2, Decomposition) {
// Create example sequence
Sequence sequence;
for (size_t i = 0; i < 16; i++) {
double x = (double)i / 16. - 0.99, y = x;
double x = (1.0/ 16)*i - 0.99, y = x;
sequence[x] = y;
}
@ -144,11 +166,11 @@ TEST(Chebyshev2, DifferentiationMatrix3) {
// Trefethen00book, p.55
const size_t N = 3;
Matrix expected(N, N);
// Differentiation matrix computed from Chebfun
// Differentiation matrix computed from chebfun
expected << 1.5000, -2.0000, 0.5000, //
0.5000, -0.0000, -0.5000, //
-0.5000, 2.0000, -1.5000;
// multiply by -1 since the cheb points have a phase shift wrt Trefethen
// multiply by -1 since the chebyshev points have a phase shift wrt Trefethen
// This was verified with chebfun
expected = -expected;
@ -167,7 +189,7 @@ TEST(Chebyshev2, DerivativeMatrix6) {
0.3820, -0.8944, 1.6180, 0.1708, -2.0000, 0.7236, //
-0.2764, 0.6180, -0.8944, 2.0000, 1.1708, -2.6180, //
0.5000, -1.1056, 1.5279, -2.8944, 10.4721, -8.5000;
// multiply by -1 since the cheb points have a phase shift wrt Trefethen
// multiply by -1 since the chebyshev points have a phase shift wrt Trefethen
// This was verified with chebfun
expected = -expected;
@ -252,7 +274,7 @@ TEST(Chebyshev2, DerivativeWeights2) {
Weights dWeights2 = Chebyshev2::DerivativeWeights(N, x2, a, b);
EXPECT_DOUBLES_EQUAL(fprime(x2), dWeights2 * fvals, 1e-8);
// test if derivative calculation and cheb point is correct
// test if derivative calculation and Chebyshev point is correct
double x3 = Chebyshev2::Point(N, 3, a, b);
Weights dWeights3 = Chebyshev2::DerivativeWeights(N, x3, a, b);
EXPECT_DOUBLES_EQUAL(fprime(x3), dWeights3 * fvals, 1e-8);

28
gtsam/discrete/AlgebraicDecisionTree.cpp Normal file
View File

@ -0,0 +1,28 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file AlgebraicDecisionTree.cpp
* @date Feb 20, 2022
* @author Mike Sheffler
* @author Duy-Nguyen Ta
* @author Frank Dellaert
*/
#include "AlgebraicDecisionTree.h"
#include <gtsam/base/types.h>
namespace gtsam {
template class AlgebraicDecisionTree<Key>;
} // namespace gtsam

4
gtsam/discrete/AlgebraicDecisionTree.h
View File

@ -127,7 +127,7 @@ namespace gtsam {
return map.at(label);
};
std::function<double(const double&)> op = Ring::id;
this->root_ = this->template convertFrom(other.root_, L_of_M, op);
this->root_ = DecisionTree<L, double>::convertFrom(other.root_, L_of_M, op);
}
/** sum */
@ -160,7 +160,7 @@ namespace gtsam {
const typename Base::LabelFormatter& labelFormatter =
&DefaultFormatter) const {
auto valueFormatter = [](const double& v) {
return (boost::format("%4.4g") % v).str();
return (boost::format("%4.8g") % v).str();
};
Base::print(s, labelFormatter, valueFormatter);
}

2
gtsam/discrete/Assignment.h
View File

@ -33,6 +33,8 @@ namespace gtsam {
template <class L>
class Assignment : public std::map<L, size_t> {
public:
using std::map<L, size_t>::operator=;
void print(const std::string& s = "Assignment: ") const {
std::cout << s << ": ";
for (const typename Assignment::value_type& keyValue : *this)

237
gtsam/discrete/DecisionTree-inl.h
View File

@ -59,33 +59,41 @@ namespace gtsam {
/** constant stored in this leaf */
Y constant_;
/** Constructor from constant */
Leaf(const Y& constant) :
constant_(constant) {}
/** The number of assignments contained within this leaf.
* Particularly useful when leaves have been pruned.
*/
size_t nrAssignments_;
/** return the constant */
/// Constructor from constant
Leaf(const Y& constant, size_t nrAssignments = 1)
: constant_(constant), nrAssignments_(nrAssignments) {}
/// Return the constant
const Y& constant() const {
return constant_;
}
/// Return the number of assignments contained within this leaf.
size_t nrAssignments() const { return nrAssignments_; }
/// Leaf-Leaf equality
bool sameLeaf(const Leaf& q) const override {
return constant_ == q.constant_;
}
/// polymorphic equality: is q is a leaf, could be
/// polymorphic equality: is q a leaf and is it the same as this leaf?
bool sameLeaf(const Node& q) const override {
return (q.isLeaf() && q.sameLeaf(*this));
}
/** equality up to tolerance */
/// equality up to tolerance
bool equals(const Node& q, const CompareFunc& compare) const override {
const Leaf* other = dynamic_cast<const Leaf*>(&q);
if (!other) return false;
return compare(this->constant_, other->constant_);
}
/** print */
/// print
void print(const std::string& s, const LabelFormatter& labelFormatter,
const ValueFormatter& valueFormatter) const override {
std::cout << s << " Leaf " << valueFormatter(constant_) << std::endl;
@ -108,7 +116,14 @@ namespace gtsam {
/** apply unary operator */
NodePtr apply(const Unary& op) const override {
NodePtr f(new Leaf(op(constant_)));
NodePtr f(new Leaf(op(constant_), nrAssignments_));
return f;
}
/// Apply unary operator with assignment
NodePtr apply(const UnaryAssignment& op,
const Assignment<L>& assignment) const override {
NodePtr f(new Leaf(op(assignment, constant_), nrAssignments_));
return f;
}
@ -123,7 +138,8 @@ namespace gtsam {
// Applying binary operator to two leaves results in a leaf
NodePtr apply_g_op_fL(const Leaf& fL, const Binary& op) const override {
NodePtr h(new Leaf(op(fL.constant_, constant_))); // fL op gL
// fL op gL
NodePtr h(new Leaf(op(fL.constant_, constant_), nrAssignments_));
return h;
}
@ -134,7 +150,7 @@ namespace gtsam {
/** choose a branch, create new memory ! */
NodePtr choose(const L& label, size_t index) const override {
return NodePtr(new Leaf(constant()));
return NodePtr(new Leaf(constant(), nrAssignments()));
}
bool isLeaf() const override { return true; }
@ -152,7 +168,10 @@ namespace gtsam {
std::vector<NodePtr> branches_;
private:
/** incremental allSame */
/**
* Incremental allSame.
* Records if all the branches are the same leaf.
*/
size_t allSame_;
using ChoicePtr = boost::shared_ptr<const Choice>;
@ -165,15 +184,22 @@ namespace gtsam {
#endif
}
/** If all branches of a choice node f are the same, just return a branch */
/// If all branches of a choice node f are the same, just return a branch.
static NodePtr Unique(const ChoicePtr& f) {
#ifndef DT_NO_PRUNING
#ifndef GTSAM_DT_NO_PRUNING
if (f->allSame_) {
assert(f->branches().size() > 0);
NodePtr f0 = f->branches_[0];
assert(f0->isLeaf());
size_t nrAssignments = 0;
for(auto branch: f->branches()) {
assert(branch->isLeaf());
nrAssignments +=
boost::dynamic_pointer_cast<const Leaf>(branch)->nrAssignments();
}
NodePtr newLeaf(
new Leaf(boost::dynamic_pointer_cast<const Leaf>(f0)->constant()));
new Leaf(boost::dynamic_pointer_cast<const Leaf>(f0)->constant(),
nrAssignments));
return newLeaf;
} else
#endif
@ -182,15 +208,13 @@ namespace gtsam {
bool isLeaf() const override { return false; }
/** Constructor, given choice label and mandatory expected branch count */
/// Constructor, given choice label and mandatory expected branch count.
Choice(const L& label, size_t count) :
label_(label), allSame_(true) {
branches_.reserve(count);
}
/**
* Construct from applying binary op to two Choice nodes
*/
/// Construct from applying binary op to two Choice nodes.
Choice(const Choice& f, const Choice& g, const Binary& op) :
allSame_(true) {
// Choose what to do based on label
@ -218,6 +242,7 @@ namespace gtsam {
}
}
/// Return the label of this choice node.
const L& label() const {
return label_;
}
@ -239,7 +264,7 @@ namespace gtsam {
branches_.push_back(node);
}
/** print (as a tree) */
/// print (as a tree).
void print(const std::string& s, const LabelFormatter& labelFormatter,
const ValueFormatter& valueFormatter) const override {
std::cout << s << " Choice(";
@ -285,7 +310,7 @@ namespace gtsam {
return (q.isLeaf() && q.sameLeaf(*this));
}
/** equality */
/// equality
bool equals(const Node& q, const CompareFunc& compare) const override {
const Choice* other = dynamic_cast<const Choice*>(&q);
if (!other) return false;
@ -298,7 +323,7 @@ namespace gtsam {
return true;
}
/** evaluate */
/// evaluate
const Y& operator()(const Assignment<L>& x) const override {
#ifndef NDEBUG
typename Assignment<L>::const_iterator it = x.find(label_);
@ -313,21 +338,57 @@ namespace gtsam {
return (*child)(x);
}
/**
* Construct from applying unary op to a Choice node
*/
/// Construct from applying unary op to a Choice node.
Choice(const L& label, const Choice& f, const Unary& op) :
label_(label), allSame_(true) {
branches_.reserve(f.branches_.size()); // reserve space
for (const NodePtr& branch : f.branches_) push_back(branch->apply(op));
for (const NodePtr& branch : f.branches_) {
push_back(branch->apply(op));
}
}
/** apply unary operator */
/**
* @brief Constructor which accepts a UnaryAssignment op and the
* corresponding assignment.
*
* @param label The label for this node.
* @param f The original choice node to apply the op on.
* @param op Function to apply on the choice node. Takes Assignment and
* value as arguments.
* @param assignment The Assignment that will go to op.
*/
Choice(const L& label, const Choice& f, const UnaryAssignment& op,
const Assignment<L>& assignment)
: label_(label), allSame_(true) {
branches_.reserve(f.branches_.size()); // reserve space
Assignment<L> assignment_ = assignment;
for (size_t i = 0; i < f.branches_.size(); i++) {
assignment_[label_] = i; // Set assignment for label to i
const NodePtr branch = f.branches_[i];
push_back(branch->apply(op, assignment_));
// Remove the assignment so we are backtracking
auto assignment_it = assignment_.find(label_);
assignment_.erase(assignment_it);
}
}
/// apply unary operator.
NodePtr apply(const Unary& op) const override {
auto r = boost::make_shared<Choice>(label_, *this, op);
return Unique(r);
}
/// Apply unary operator with assignment
NodePtr apply(const UnaryAssignment& op,
const Assignment<L>& assignment) const override {
auto r = boost::make_shared<Choice>(label_, *this, op, assignment);
return Unique(r);
}
// Apply binary operator "h = f op g" on Choice node
// Note op is not assumed commutative so we need to keep track of order
// Simply calls apply on argument to call correct virtual method:
@ -592,11 +653,13 @@ namespace gtsam {
std::function<Y(const X&)> Y_of_X) const {
using LY = DecisionTree<L, Y>;
// ugliness below because apparently we can't have templated virtual
// functions If leaf, apply unary conversion "op" and create a unique leaf
// Ugliness below because apparently we can't have templated virtual
// functions.
// If leaf, apply unary conversion "op" and create a unique leaf.
using MXLeaf = typename DecisionTree<M, X>::Leaf;
if (auto leaf = boost::dynamic_pointer_cast<const MXLeaf>(f))
return NodePtr(new Leaf(Y_of_X(leaf->constant())));
if (auto leaf = boost::dynamic_pointer_cast<const MXLeaf>(f)) {
return NodePtr(new Leaf(Y_of_X(leaf->constant()), leaf->nrAssignments()));
}
// Check if Choice
using MXChoice = typename DecisionTree<M, X>::Choice;
@ -617,7 +680,16 @@ namespace gtsam {
}
/****************************************************************************/
// Functor performing depth-first visit without Assignment<L> argument.
/**
* Functor performing depth-first visit to each leaf with the leaf value as
* the argument.
*
* NOTE: We differentiate between leaves and assignments. Concretely, a 3
* binary variable tree will have 2^3=8 assignments, but based on pruning, it
* can have less than 8 leaves. For example, if a tree has all assignment
* values as 1, then pruning will cause the tree to have only 1 leaf yet 8
* assignments.
*/
template <typename L, typename Y>
struct Visit {
using F = std::function<void(const Y&)>;
@ -646,28 +718,74 @@ namespace gtsam {
}
/****************************************************************************/
// Functor performing depth-first visit with Assignment<L> argument.
/**
* Functor performing depth-first visit to each leaf with the Leaf object
* passed as an argument.
*
* NOTE: We differentiate between leaves and assignments. Concretely, a 3
* binary variable tree will have 2^3=8 assignments, but based on pruning, it
* can have <8 leaves. For example, if a tree has all assignment values as 1,
* then pruning will cause the tree to have only 1 leaf yet 8 assignments.
*/
template <typename L, typename Y>
struct VisitLeaf {
using F = std::function<void(const typename DecisionTree<L, Y>::Leaf&)>;
explicit VisitLeaf(F f) : f(f) {} ///< Construct from folding function.
F f; ///< folding function object.
/// Do a depth-first visit on the tree rooted at node.
void operator()(const typename DecisionTree<L, Y>::NodePtr& node) const {
using Leaf = typename DecisionTree<L, Y>::Leaf;
if (auto leaf = boost::dynamic_pointer_cast<const Leaf>(node))
return f(*leaf);
using Choice = typename DecisionTree<L, Y>::Choice;
auto choice = boost::dynamic_pointer_cast<const Choice>(node);
if (!choice)
throw std::invalid_argument("DecisionTree::VisitLeaf: Invalid NodePtr");
for (auto&& branch : choice->branches()) (*this)(branch); // recurse!
}
};
template <typename L, typename Y>
template <typename Func>
void DecisionTree<L, Y>::visitLeaf(Func f) const {
VisitLeaf<L, Y> visit(f);
visit(root_);
}
/****************************************************************************/
/**
* Functor performing depth-first visit to each leaf with the leaf's
* `Assignment<L>` and value passed as arguments.
*
* NOTE: Follows the same pruning semantics as `visit`.
*/
template <typename L, typename Y>
struct VisitWith {
using Choices = Assignment<L>;
using F = std::function<void(const Choices&, const Y&)>;
using F = std::function<void(const Assignment<L>&, const Y&)>;
explicit VisitWith(F f) : f(f) {} ///< Construct from folding function.
Choices choices; ///< Assignment, mutating through recursion.
F f; ///< folding function object.
Assignment<L> assignment; ///< Assignment, mutating through recursion.
F f; ///< folding function object.
/// Do a depth-first visit on the tree rooted at node.
void operator()(const typename DecisionTree<L, Y>::NodePtr& node) {
using Leaf = typename DecisionTree<L, Y>::Leaf;
if (auto leaf = boost::dynamic_pointer_cast<const Leaf>(node))
return f(choices, leaf->constant());
return f(assignment, leaf->constant());
using Choice = typename DecisionTree<L, Y>::Choice;
auto choice = boost::dynamic_pointer_cast<const Choice>(node);
if (!choice)
throw std::invalid_argument("DecisionTree::VisitWith: Invalid NodePtr");
for (size_t i = 0; i < choice->nrChoices(); i++) {
choices[choice->label()] = i; // Set assignment for label to i
assignment[choice->label()] = i; // Set assignment for label to i
(*this)(choice->branches()[i]); // recurse!
// Remove the choice so we are backtracking
auto choice_it = assignment.find(choice->label());
assignment.erase(choice_it);
}
}
};
@ -679,6 +797,14 @@ namespace gtsam {
visit(root_);
}
/****************************************************************************/
template <typename L, typename Y>
size_t DecisionTree<L, Y>::nrLeaves() const {
size_t total = 0;
visit([&total](const Y& node) { total += 1; });
return total;
}
/****************************************************************************/
// fold is just done with a visit
template <typename L, typename Y>
@ -689,12 +815,26 @@ namespace gtsam {
}
/****************************************************************************/
// labels is just done with a visit
/**
* Get (partial) labels by performing a visit.
*
* This method performs a depth-first search to go to every leaf and records
* the keys assignment which leads to that leaf. Since the tree can be pruned,
* there might be a leaf at a lower depth which results in a partial
* assignment (i.e. not all keys are specified).
*
* E.g. given a tree with 3 keys, there may be a branch where the 3rd key has
* the same values for all the leaves. This leads to the branch being pruned
* so we get a leaf which is arrived at by just the first 2 keys and their
* assignments.
*/
template <typename L, typename Y>
std::set<L> DecisionTree<L, Y>::labels() const {
std::set<L> unique;
auto f = [&](const Assignment<L>& choices, const Y&) {
for (auto&& kv : choices) unique.insert(kv.first);
auto f = [&](const Assignment<L>& assignment, const Y&) {
for (auto&& kv : assignment) {
unique.insert(kv.first);
}
};
visitWith(f);
return unique;
@ -734,6 +874,19 @@ namespace gtsam {
return DecisionTree(root_->apply(op));
}
/// Apply unary operator with assignment
template <typename L, typename Y>
DecisionTree<L, Y> DecisionTree<L, Y>::apply(
const UnaryAssignment& op) const {
// It is unclear what should happen if tree is empty:
if (empty()) {
throw std::runtime_error(
"DecisionTree::apply(unary op) undefined for empty tree.");
}
Assignment<L> assignment;
return DecisionTree(root_->apply(op, assignment));
}
/****************************************************************************/
template<typename L, typename Y>
DecisionTree<L, Y> DecisionTree<L, Y>::apply(const DecisionTree& g,

75
gtsam/discrete/DecisionTree.h
View File

@ -54,6 +54,7 @@ namespace gtsam {
/** Handy typedefs for unary and binary function types */
using Unary = std::function<Y(const Y&)>;
using UnaryAssignment = std::function<Y(const Assignment<L>&, const Y&)>;
using Binary = std::function<Y(const Y&, const Y&)>;
/** A label annotated with cardinality */
@ -103,6 +104,8 @@ namespace gtsam {
&DefaultCompare) const = 0;
virtual const Y& operator()(const Assignment<L>& x) const = 0;
virtual Ptr apply(const Unary& op) const = 0;
virtual Ptr apply(const UnaryAssignment& op,
const Assignment<L>& assignment) const = 0;
virtual Ptr apply_f_op_g(const Node&, const Binary&) const = 0;
virtual Ptr apply_g_op_fL(const Leaf&, const Binary&) const = 0;
virtual Ptr apply_g_op_fC(const Choice&, const Binary&) const = 0;
@ -150,7 +153,7 @@ namespace gtsam {
/** Create a constant */
explicit DecisionTree(const Y& y);
/** Create a new leaf function splitting on a variable */
/// Create tree with 2 assignments `y1`, `y2`, splitting on variable `label`
DecisionTree(const L& label, const Y& y1, const Y& y2);
/** Allow Label+Cardinality for convenience */
@ -216,9 +219,8 @@ namespace gtsam {
/// @name Standard Interface
/// @{
/** Make virtual */
virtual ~DecisionTree() {
}
/// Make virtual
virtual ~DecisionTree() {}
/// Check if tree is empty.
bool empty() const { return !root_; }
@ -231,11 +233,13 @@ namespace gtsam {
/**
* @brief Visit all leaves in depth-first fashion.
*
* @param f side-effect taking a value.
*
* @note Due to pruning, leaves might not exhaust choices.
*
*
* @param f (side-effect) Function taking a value.
*
* @note Due to pruning, the number of leaves may not be the same as the
* number of assignments. E.g. if we have a tree on 2 binary variables with
* all values being 1, then there are 2^2=4 assignments, but only 1 leaf.
*
* Example:
* int sum = 0;
* auto visitor = [&](int y) { sum += y; };
@ -246,19 +250,41 @@ namespace gtsam {
/**
* @brief Visit all leaves in depth-first fashion.
*
* @param f side-effect taking an assignment and a value.
*
* @note Due to pruning, leaves might not exhaust choices.
*
*
* @param f (side-effect) Function taking the leaf node pointer.
*
* @note Due to pruning, the number of leaves may not be the same as the
* number of assignments. E.g. if we have a tree on 2 binary variables with
* all values being 1, then there are 2^2=4 assignments, but only 1 leaf.
*
* Example:
* int sum = 0;
* auto visitor = [&](const Assignment<L>& choices, int y) { sum += y; };
* auto visitor = [&](int y) { sum += y; };
* tree.visitWith(visitor);
*/
template <typename Func>
void visitLeaf(Func f) const;
/**
* @brief Visit all leaves in depth-first fashion.
*
* @param f (side-effect) Function taking an assignment and a value.
*
* @note Due to pruning, the number of leaves may not be the same as the
* number of assignments. E.g. if we have a tree on 2 binary variables with
* all values being 1, then there are 2^2=4 assignments, but only 1 leaf.
*
* Example:
* int sum = 0;
* auto visitor = [&](const Assignment<L>& assignment, int y) { sum += y; };
* tree.visitWith(visitor);
*/
template <typename Func>
void visitWith(Func f) const;
/// Return the number of leaves in the tree.
size_t nrLeaves() const;
/**
* @brief Fold a binary function over the tree, returning accumulator.
*
@ -269,7 +295,7 @@ namespace gtsam {
*
* @note X is always passed by value.
* @note Due to pruning, leaves might not exhaust choices.
*
*
* Example:
* auto add = [](const double& y, double x) { return y + x; };
* double sum = tree.fold(add, 0.0);
@ -283,6 +309,16 @@ namespace gtsam {
/** apply Unary operation "op" to f */
DecisionTree apply(const Unary& op) const;
/**
* @brief Apply Unary operation "op" to f while also providing the
* corresponding assignment.
*
* @param op Function which takes Assignment<L> and Y as input and returns
* object of type Y.
* @return DecisionTree
*/
DecisionTree apply(const UnaryAssignment& op) const;
/** apply binary operation "op" to f and g */
DecisionTree apply(const DecisionTree& g, const Binary& op) const;
@ -337,6 +373,13 @@ namespace gtsam {
return f.apply(op);
}
/// Apply unary operator `op` with Assignment to DecisionTree `f`.
template<typename L, typename Y>
DecisionTree<L, Y> apply(const DecisionTree<L, Y>& f,
const typename DecisionTree<L, Y>::UnaryAssignment& op) {
return f.apply(op);
}
/// Apply binary operator `op` to DecisionTree `f`.
template<typename L, typename Y>
DecisionTree<L, Y> apply(const DecisionTree<L, Y>& f,

43
gtsam/discrete/DecisionTreeFactor.cpp
View File

@ -156,10 +156,7 @@ namespace gtsam {
std::vector<std::pair<DiscreteValues, double>> DecisionTreeFactor::enumerate()
const {
// Get all possible assignments
std::vector<std::pair<Key, size_t>> pairs;
for (auto& key : keys()) {
pairs.emplace_back(key, cardinalities_.at(key));
}
std::vector<std::pair<Key, size_t>> pairs = discreteKeys();
// Reverse to make cartesian product output a more natural ordering.
std::vector<std::pair<Key, size_t>> rpairs(pairs.rbegin(), pairs.rend());
const auto assignments = DiscreteValues::CartesianProduct(rpairs);
@ -289,5 +286,43 @@ namespace gtsam {
AlgebraicDecisionTree<Key>(keys, table),
cardinalities_(keys.cardinalities()) {}
/* ************************************************************************ */
DecisionTreeFactor DecisionTreeFactor::prune(size_t maxNrAssignments) const {
const size_t N = maxNrAssignments;
// Get the probabilities in the decision tree so we can threshold.
std::vector<double> probabilities;
this->visitLeaf([&](const Leaf& leaf) {
size_t nrAssignments = leaf.nrAssignments();
double prob = leaf.constant();
probabilities.insert(probabilities.end(), nrAssignments, prob);
});
// The number of probabilities can be lower than max_leaves
if (probabilities.size() <= N) {
return *this;
}
std::sort(probabilities.begin(), probabilities.end(),
std::greater<double>{});
double threshold = probabilities[N - 1];
// Now threshold the decision tree
size_t total = 0;
auto thresholdFunc = [threshold, &total, N](const double& value) {
if (value < threshold || total >= N) {
return 0.0;
} else {
total += 1;
return value;
}
};
DecisionTree<Key, double> thresholded(*this, thresholdFunc);
// Create pruned decision tree factor and return.
return DecisionTreeFactor(this->discreteKeys(), thresholded);
}
/* ************************************************************************ */
} // namespace gtsam

20
gtsam/discrete/DecisionTreeFactor.h
View File

@ -170,6 +170,26 @@ namespace gtsam {
/// Return all the discrete keys associated with this factor.
DiscreteKeys discreteKeys() const;
/**
* @brief Prune the decision tree of discrete variables.
*
* Pruning will set the leaves to be "pruned" to 0 indicating a 0
* probability. An assignment is pruned if it is not in the top
* `maxNrAssignments` values.
*
* A violation can occur if there are more
* duplicate values than `maxNrAssignments`. A violation here is the need to
* un-prune the decision tree (e.g. all assignment values are 1.0). We could
* have another case where some subset of duplicates exist (e.g. for a tree
* with 8 assignments we have 1, 1, 1, 1, 0.8, 0.7, 0.6, 0.5), but this is
* not a violation since the for `maxNrAssignments=5` the top values are (1,
* 0.8).
*
* @param maxNrAssignments The maximum number of assignments to keep.
* @return DecisionTreeFactor
*/
DecisionTreeFactor prune(size_t maxNrAssignments) const;
/// @}
/// @name Wrapper support
/// @{

4
gtsam/discrete/DiscreteConditional.cpp
View File

@ -225,13 +225,13 @@ DecisionTreeFactor::shared_ptr DiscreteConditional::likelihood(
/* ****************************************************************************/
DecisionTreeFactor::shared_ptr DiscreteConditional::likelihood(
size_t parent_value) const {
size_t frontal) const {
if (nrFrontals() != 1)
throw std::invalid_argument(
"Single value likelihood can only be invoked on single-variable "
"conditional");
DiscreteValues values;
values.emplace(keys_[0], parent_value);
values.emplace(keys_[0], frontal);
return likelihood(values);
}

2
gtsam/discrete/DiscreteConditional.h
View File

@ -177,7 +177,7 @@ class GTSAM_EXPORT DiscreteConditional
const DiscreteValues& frontalValues) const;
/** Single variable version of likelihood. */
DecisionTreeFactor::shared_ptr likelihood(size_t parent_value) const;
DecisionTreeFactor::shared_ptr likelihood(size_t frontal) const;
/**
* sample

2
gtsam/discrete/DiscreteJunctionTree.h
View File

@ -16,6 +16,8 @@
* @author Richard Roberts
*/
#pragma once
#include <gtsam/discrete/DiscreteFactorGraph.h>
#include <gtsam/discrete/DiscreteBayesTree.h>
#include <gtsam/inference/JunctionTree.h>

2
gtsam/discrete/DiscreteKey.h
View File

@ -72,5 +72,5 @@ namespace gtsam {
}; // DiscreteKeys
/// Create a list from two keys
DiscreteKeys operator&(const DiscreteKey& key1, const DiscreteKey& key2);
GTSAM_EXPORT DiscreteKeys operator&(const DiscreteKey& key1, const DiscreteKey& key2);
}

4
gtsam/discrete/DiscreteLookupDAG.h
View File

@ -36,7 +36,7 @@ class DiscreteBayesNet;
* Inherits from discrete conditional for convenience, but is not normalized.
* Is used in the max-product algorithm.
*/
class DiscreteLookupTable : public DiscreteConditional {
class GTSAM_EXPORT DiscreteLookupTable : public DiscreteConditional {
public:
using This = DiscreteLookupTable;
using shared_ptr = boost::shared_ptr<This>;
@ -46,7 +46,7 @@ class DiscreteLookupTable : public DiscreteConditional {
* @brief Construct a new Discrete Lookup Table object
*
* @param nFrontals number of frontal variables
* @param keys a orted list of gtsam::Keys
* @param keys a sorted list of gtsam::Keys
* @param potentials the algebraic decision tree with lookup values
*/
DiscreteLookupTable(size_t nFrontals, const DiscreteKeys& keys,

2
gtsam/discrete/DiscreteMarginals.h
View File

@ -29,7 +29,7 @@ namespace gtsam {
/**
* A class for computing marginals of variables in a DiscreteFactorGraph
*/
class GTSAM_EXPORT DiscreteMarginals {
class DiscreteMarginals {
protected:

2
gtsam/discrete/DiscreteValues.h
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@ -37,7 +37,7 @@ namespace gtsam {
* stores cardinality of a Discrete variable. It should be handled naturally in
* the new class DiscreteValue, as the variable's type (domain)
*/
class DiscreteValues : public Assignment<Key> {
class GTSAM_EXPORT DiscreteValues : public Assignment<Key> {
public:
using Base = Assignment<Key>; // base class

19
gtsam/discrete/discrete.i
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@ -70,7 +70,7 @@ virtual class DecisionTreeFactor : gtsam::DiscreteFactor {
string dot(
const gtsam::KeyFormatter& keyFormatter = gtsam::DefaultKeyFormatter,
bool showZero = true) const;
std::vector<std::pair<DiscreteValues, double>> enumerate() const;
std::vector<std::pair<gtsam::DiscreteValues, double>> enumerate() const;
string markdown(const gtsam::KeyFormatter& keyFormatter =
gtsam::DefaultKeyFormatter) const;
string markdown(const gtsam::KeyFormatter& keyFormatter,
@ -97,7 +97,7 @@ virtual class DiscreteConditional : gtsam::DecisionTreeFactor {
const gtsam::Ordering& orderedKeys);
gtsam::DiscreteConditional operator*(
const gtsam::DiscreteConditional& other) const;
DiscreteConditional marginal(gtsam::Key key) const;
gtsam::DiscreteConditional marginal(gtsam::Key key) const;
void print(string s = "Discrete Conditional\n",
const gtsam::KeyFormatter& keyFormatter =
gtsam::DefaultKeyFormatter) const;
@ -269,13 +269,16 @@ class DiscreteFactorGraph {
gtsam::DiscreteLookupDAG maxProduct(gtsam::Ordering::OrderingType type);
gtsam::DiscreteLookupDAG maxProduct(const gtsam::Ordering& ordering);
gtsam::DiscreteBayesNet eliminateSequential();
gtsam::DiscreteBayesNet eliminateSequential(const gtsam::Ordering& ordering);
std::pair<gtsam::DiscreteBayesNet, gtsam::DiscreteFactorGraph>
gtsam::DiscreteBayesNet* eliminateSequential();
gtsam::DiscreteBayesNet* eliminateSequential(gtsam::Ordering::OrderingType type);
gtsam::DiscreteBayesNet* eliminateSequential(const gtsam::Ordering& ordering);
pair<gtsam::DiscreteBayesNet*, gtsam::DiscreteFactorGraph*>
eliminatePartialSequential(const gtsam::Ordering& ordering);
gtsam::DiscreteBayesTree eliminateMultifrontal();
gtsam::DiscreteBayesTree eliminateMultifrontal(const gtsam::Ordering& ordering);
std::pair<gtsam::DiscreteBayesTree, gtsam::DiscreteFactorGraph>
gtsam::DiscreteBayesTree* eliminateMultifrontal();
gtsam::DiscreteBayesTree* eliminateMultifrontal(gtsam::Ordering::OrderingType type);
gtsam::DiscreteBayesTree* eliminateMultifrontal(const gtsam::Ordering& ordering);
pair<gtsam::DiscreteBayesTree*, gtsam::DiscreteFactorGraph*>
eliminatePartialMultifrontal(const gtsam::Ordering& ordering);
string dot(

4
gtsam/discrete/tests/testAlgebraicDecisionTree.cpp
View File

@ -20,7 +20,7 @@
#include <gtsam/discrete/DiscreteKey.h> // make sure we have traits
#include <gtsam/discrete/DiscreteValues.h>
// headers first to make sure no missing headers
//#define DT_NO_PRUNING
//#define GTSAM_DT_NO_PRUNING
#include <gtsam/discrete/AlgebraicDecisionTree.h>
#include <gtsam/discrete/DecisionTree-inl.h> // for convert only
#define DISABLE_TIMING
@ -318,7 +318,7 @@ TEST(ADT, factor_graph) {
dot(fg, "Marginalized-3E");
fg = fg.combine(L, &add_);
dot(fg, "Marginalized-2L");
EXPECT(adds = 54);
LONGS_EQUAL(49, adds);
gttoc_(marg);
tictoc_getNode(margNode, marg);
elapsed = margNode->secs() + margNode->wall();

141
gtsam/discrete/tests/testDecisionTree.cpp
View File

@ -17,17 +17,19 @@
* @date Jan 30, 2012
*/
#include <boost/assign/std/vector.hpp>
using namespace boost::assign;
// #define DT_DEBUG_MEMORY
// #define GTSAM_DT_NO_PRUNING
#define DISABLE_DOT
#include <gtsam/discrete/DecisionTree-inl.h>
#include <CppUnitLite/TestHarness.h>
#include <gtsam/base/Testable.h>
#include <gtsam/discrete/Signature.h>
// #define DT_DEBUG_MEMORY
// #define DT_NO_PRUNING
#define DISABLE_DOT
#include <gtsam/discrete/DecisionTree-inl.h>
#include <CppUnitLite/TestHarness.h>
#include <boost/assign/std/vector.hpp>
using namespace boost::assign;
using namespace std;
using namespace gtsam;
@ -88,6 +90,7 @@ struct DT : public DecisionTree<string, int> {
auto valueFormatter = [](const int& v) {
return (boost::format("%d") % v).str();
};
std::cout << s;
Base::print("", keyFormatter, valueFormatter);
}
/// Equality method customized to int node type
@ -148,9 +151,9 @@ TEST(DecisionTree, example) {
DOT(notb);
// Check supplying empty trees yields an exception
CHECK_EXCEPTION(apply(empty, &Ring::id), std::runtime_error);
CHECK_EXCEPTION(apply(empty, a, &Ring::mul), std::runtime_error);
CHECK_EXCEPTION(apply(a, empty, &Ring::mul), std::runtime_error);
CHECK_EXCEPTION(gtsam::apply(empty, &Ring::id), std::runtime_error);
CHECK_EXCEPTION(gtsam::apply(empty, a, &Ring::mul), std::runtime_error);
CHECK_EXCEPTION(gtsam::apply(a, empty, &Ring::mul), std::runtime_error);
// apply, two nodes, in natural order
DT anotb = apply(a, notb, &Ring::mul);
@ -320,6 +323,49 @@ TEST(DecisionTree, Containers) {
StringContainerTree converted(stringIntTree, container_of_int);
}
/* ************************************************************************** */
// Test nrAssignments.
TEST(DecisionTree, NrAssignments) {
pair<string, size_t> A("A", 2), B("B", 2), C("C", 2);
DT tree({A, B, C}, "1 1 1 1 1 1 1 1");
EXPECT(tree.root_->isLeaf());
auto leaf = boost::dynamic_pointer_cast<const DT::Leaf>(tree.root_);
EXPECT_LONGS_EQUAL(8, leaf->nrAssignments());
DT tree2({C, B, A}, "1 1 1 2 3 4 5 5");
/* The tree is
Choice(C)
0 Choice(B)
0 0 Leaf 1
0 1 Choice(A)
0 1 0 Leaf 1
0 1 1 Leaf 2
1 Choice(B)
1 0 Choice(A)
1 0 0 Leaf 3
1 0 1 Leaf 4
1 1 Leaf 5
*/
auto root = boost::dynamic_pointer_cast<const DT::Choice>(tree2.root_);
CHECK(root);
auto choice0 = boost::dynamic_pointer_cast<const DT::Choice>(root->branches()[0]);
CHECK(choice0);
EXPECT(choice0->branches()[0]->isLeaf());
auto choice00 = boost::dynamic_pointer_cast<const DT::Leaf>(choice0->branches()[0]);
CHECK(choice00);
EXPECT_LONGS_EQUAL(2, choice00->nrAssignments());
auto choice1 = boost::dynamic_pointer_cast<const DT::Choice>(root->branches()[1]);
CHECK(choice1);
auto choice10 = boost::dynamic_pointer_cast<const DT::Choice>(choice1->branches()[0]);
CHECK(choice10);
auto choice11 = boost::dynamic_pointer_cast<const DT::Leaf>(choice1->branches()[1]);
CHECK(choice11);
EXPECT(choice11->isLeaf());
EXPECT_LONGS_EQUAL(2, choice11->nrAssignments());
}
/* ************************************************************************** */
// Test visit.
TEST(DecisionTree, visit) {
@ -344,6 +390,44 @@ TEST(DecisionTree, visitWith) {
EXPECT_DOUBLES_EQUAL(6.0, sum, 1e-9);
}
/* ************************************************************************** */
// Test visit, with Choices argument.
TEST(DecisionTree, VisitWithPruned) {
// Create pruned tree
std::pair<string, size_t> A("A", 2), B("B", 2), C("C", 2);
std::vector<std::pair<string, size_t>> labels = {C, B, A};
std::vector<int> nodes = {0, 0, 2, 3, 4, 4, 6, 7};
DT tree(labels, nodes);
std::vector<Assignment<string>> choices;
auto func = [&](const Assignment<string>& choice, const int& d) {
choices.push_back(choice);
};
tree.visitWith(func);
EXPECT_LONGS_EQUAL(6, choices.size());
Assignment<string> expectedAssignment;
expectedAssignment = {{"B", 0}, {"C", 0}};
EXPECT(expectedAssignment == choices.at(0));
expectedAssignment = {{"A", 0}, {"B", 1}, {"C", 0}};
EXPECT(expectedAssignment == choices.at(1));
expectedAssignment = {{"A", 1}, {"B", 1}, {"C", 0}};
EXPECT(expectedAssignment == choices.at(2));
expectedAssignment = {{"B", 0}, {"C", 1}};
EXPECT(expectedAssignment == choices.at(3));
expectedAssignment = {{"A", 0}, {"B", 1}, {"C", 1}};
EXPECT(expectedAssignment == choices.at(4));
expectedAssignment = {{"A", 1}, {"B", 1}, {"C", 1}};
EXPECT(expectedAssignment == choices.at(5));
}
/* ************************************************************************** */
// Test fold.
TEST(DecisionTree, fold) {
@ -411,6 +495,43 @@ TEST(DecisionTree, threshold) {
EXPECT_LONGS_EQUAL(2, thresholded.fold(count, 0));
}
/* ************************************************************************** */
// Test apply with assignment.
TEST(DecisionTree, ApplyWithAssignment) {
// Create three level tree
vector<DT::LabelC> keys;
keys += DT::LabelC("C", 2), DT::LabelC("B", 2), DT::LabelC("A", 2);
DT tree(keys, "1 2 3 4 5 6 7 8");
DecisionTree<string, double> probTree(
keys, "0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08");
double threshold = 0.045;
// We test pruning one tree by indexing into another.
auto pruner = [&](const Assignment<string>& choices, const int& x) {
// Prune out all the leaves with even numbers
if (probTree(choices) < threshold) {
return 0;
} else {
return x;
}
};
DT prunedTree = tree.apply(pruner);
DT expectedTree(keys, "0 0 0 0 5 6 7 8");
EXPECT(assert_equal(expectedTree, prunedTree));
size_t count = 0;
auto counter = [&](const Assignment<string>& choices, const int& x) {
count += 1;
return x;
};
DT prunedTree2 = prunedTree.apply(counter);
// Check if apply doesn't enumerate all leaves.
EXPECT_LONGS_EQUAL(5, count);
}
/* ************************************************************************* */
int main() {
TestResult tr;

40
gtsam/discrete/tests/testDecisionTreeFactor.cpp
View File

@ -107,12 +107,47 @@ TEST(DecisionTreeFactor, enumerate) {
}
/* ************************************************************************* */
TEST(DiscreteFactorGraph, DotWithNames) {
// Check pruning of the decision tree works as expected.
TEST(DecisionTreeFactor, Prune) {
DiscreteKey A(1, 2), B(2, 2), C(3, 2);
DecisionTreeFactor f(A & B & C, "1 5 3 7 2 6 4 8");
// Only keep the leaves with the top 5 values.
size_t maxNrAssignments = 5;
auto pruned5 = f.prune(maxNrAssignments);
// Pruned leaves should be 0
DecisionTreeFactor expected(A & B & C, "0 5 0 7 0 6 4 8");
EXPECT(assert_equal(expected, pruned5));
// Check for more extreme pruning where we only keep the top 2 leaves
maxNrAssignments = 2;
auto pruned2 = f.prune(maxNrAssignments);
DecisionTreeFactor expected2(A & B & C, "0 0 0 7 0 0 0 8");
EXPECT(assert_equal(expected2, pruned2));
DiscreteKey D(4, 2);
DecisionTreeFactor factor(
D & C & B & A,
"0.0 0.0 0.0 0.60658897 0.61241912 0.61241969 0.61247685 0.61247742 0.0 "
"0.0 0.0 0.99995287 1.0 1.0 1.0 1.0");
DecisionTreeFactor expected3(
D & C & B & A,
"0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 "
"0.999952870000 1.0 1.0 1.0 1.0");
maxNrAssignments = 5;
auto pruned3 = factor.prune(maxNrAssignments);
EXPECT(assert_equal(expected3, pruned3));
}
/* ************************************************************************* */
TEST(DecisionTreeFactor, DotWithNames) {
DiscreteKey A(12, 3), B(5, 2);
DecisionTreeFactor f(A & B, "1 2 3 4 5 6");
auto formatter = [](Key key) { return key == 12 ? "A" : "B"; };
for (bool showZero:{true, false}) {
for (bool showZero:{true, false}) {
string actual = f.dot(formatter, showZero);
// pretty weak test, as ids are pointers and not stable across platforms.
string expected = "digraph G {";
@ -194,4 +229,3 @@ int main() {
return TestRegistry::runAllTests(tr);
}
/* ************************************************************************* */

1
gtsam/discrete/tests/testDiscreteBayesNet.cpp
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@ -150,7 +150,6 @@ TEST(DiscreteBayesNet, Dot) {
fragment.add((Either | Tuberculosis, LungCancer) = "F T T T");
string actual = fragment.dot();
cout << actual << endl;
EXPECT(actual ==
"digraph {\n"
" size=\"5,5\";\n"

22
gtsam/discrete/tests/testDiscreteBayesTree.cpp
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@ -243,27 +243,27 @@ TEST(DiscreteBayesTree, Dot) {
string actual = self.bayesTree->dot();
EXPECT(actual ==
"digraph G{\n"
"0[label=\"13,11,6,7\"];\n"
"0[label=\"13, 11, 6, 7\"];\n"
"0->1\n"
"1[label=\"14 : 11,13\"];\n"
"1[label=\"14 : 11, 13\"];\n"
"1->2\n"
"2[label=\"9,12 : 14\"];\n"
"2[label=\"9, 12 : 14\"];\n"
"2->3\n"
"3[label=\"3 : 9,12\"];\n"
"3[label=\"3 : 9, 12\"];\n"
"2->4\n"
"4[label=\"2 : 9,12\"];\n"
"4[label=\"2 : 9, 12\"];\n"
"2->5\n"
"5[label=\"8 : 12,14\"];\n"
"5[label=\"8 : 12, 14\"];\n"
"5->6\n"
"6[label=\"1 : 8,12\"];\n"
"6[label=\"1 : 8, 12\"];\n"
"5->7\n"
"7[label=\"0 : 8,12\"];\n"
"7[label=\"0 : 8, 12\"];\n"
"1->8\n"
"8[label=\"10 : 13,14\"];\n"
"8[label=\"10 : 13, 14\"];\n"
"8->9\n"
"9[label=\"5 : 10,13\"];\n"
"9[label=\"5 : 10, 13\"];\n"
"8->10\n"
"10[label=\"4 : 10,13\"];\n"
"10[label=\"4 : 10, 13\"];\n"
"}");
}

1
gtsam/geometry/BearingRange.h
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@ -22,6 +22,7 @@
#include <gtsam/base/Testable.h>
#include <gtsam/base/OptionalJacobian.h>
#include <boost/concept/assert.hpp>
#include <boost/serialization/nvp.hpp>
#include <iostream>
namespace gtsam {

3
gtsam/geometry/Cal3Bundler.h
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@ -41,6 +41,9 @@ class GTSAM_EXPORT Cal3Bundler : public Cal3 {
public:
enum { dimension = 3 };
///< shared pointer to stereo calibration object
using shared_ptr = boost::shared_ptr<Cal3Bundler>;
/// @name Standard Constructors
/// @{

4
gtsam/geometry/Cal3DS2.h
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@ -21,6 +21,7 @@
#pragma once
#include <gtsam/geometry/Cal3DS2_Base.h>
#include <boost/shared_ptr.hpp>
namespace gtsam {
@ -37,6 +38,9 @@ class GTSAM_EXPORT Cal3DS2 : public Cal3DS2_Base {
public:
enum { dimension = 9 };
///< shared pointer to stereo calibration object
using shared_ptr = boost::shared_ptr<Cal3DS2>;
/// @name Standard Constructors
/// @{

4
gtsam/geometry/Cal3DS2_Base.h
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@ -21,6 +21,7 @@
#include <gtsam/geometry/Cal3.h>
#include <gtsam/geometry/Point2.h>
#include <boost/shared_ptr.hpp>
namespace gtsam {
@ -47,6 +48,9 @@ class GTSAM_EXPORT Cal3DS2_Base : public Cal3 {
public:
enum { dimension = 9 };
///< shared pointer to stereo calibration object
using shared_ptr = boost::shared_ptr<Cal3DS2_Base>;
/// @name Standard Constructors
/// @{

2
gtsam/geometry/Cal3Fisheye.h
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@ -22,6 +22,8 @@
#include <gtsam/geometry/Cal3.h>
#include <gtsam/geometry/Point2.h>
#include <boost/shared_ptr.hpp>
#include <string>
namespace gtsam {

3
gtsam/geometry/Cal3Unified.h
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@ -52,6 +52,9 @@ class GTSAM_EXPORT Cal3Unified : public Cal3DS2_Base {
public:
enum { dimension = 10 };
///< shared pointer to stereo calibration object
using shared_ptr = boost::shared_ptr<Cal3Unified>;
/// @name Standard Constructors
/// @{

2
gtsam/geometry/Cyclic.h
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@ -15,6 +15,8 @@
* @author Frank Dellaert
**/
#pragma once
#include <gtsam/base/Group.h>
#include <gtsam/base/Testable.h>

2
gtsam/geometry/Line3.cpp
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@ -117,4 +117,4 @@ Line3 transformTo(const Pose3 &wTc, const Line3 &wL,
return Line3(cRl, c_ab[0], c_ab[1]);
}
}
} // namespace gtsam

29
gtsam/geometry/Line3.h
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@ -21,12 +21,27 @@
namespace gtsam {
class Line3;
/**
* Transform a line from world to camera frame
* @param wTc - Pose3 of camera in world frame
* @param wL - Line3 in world frame
* @param Dpose - OptionalJacobian of transformed line with respect to p
* @param Dline - OptionalJacobian of transformed line with respect to l
* @return Transformed line in camera frame
*/
GTSAM_EXPORT Line3 transformTo(const Pose3 &wTc, const Line3 &wL,
OptionalJacobian<4, 6> Dpose = boost::none,
OptionalJacobian<4, 4> Dline = boost::none);
/**
* A 3D line (R,a,b) : (Rot3,Scalar,Scalar)
* @addtogroup geometry
* \nosubgrouping
*/
class Line3 {
class GTSAM_EXPORT Line3 {
private:
Rot3 R_; // Rotation of line about x and y in world frame
double a_, b_; // Intersection of line with the world x-y plane rotated by R_
@ -136,18 +151,6 @@ class Line3 {
OptionalJacobian<4, 4> Dline);
};
/**
* Transform a line from world to camera frame
* @param wTc - Pose3 of camera in world frame
* @param wL - Line3 in world frame
* @param Dpose - OptionalJacobian of transformed line with respect to p
* @param Dline - OptionalJacobian of transformed line with respect to l
* @return Transformed line in camera frame
*/
Line3 transformTo(const Pose3 &wTc, const Line3 &wL,
OptionalJacobian<4, 6> Dpose = boost::none,
OptionalJacobian<4, 4> Dline = boost::none);
template<>
struct traits<Line3> : public internal::Manifold<Line3> {};

16
gtsam/geometry/PinholeCamera.h
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@ -30,7 +30,7 @@ namespace gtsam {
* \nosubgrouping
*/
template<typename Calibration>
class GTSAM_EXPORT PinholeCamera: public PinholeBaseK<Calibration> {
class PinholeCamera: public PinholeBaseK<Calibration> {
public:
@ -230,13 +230,15 @@ public:
Point2 _project2(const POINT& pw, OptionalJacobian<2, dimension> Dcamera,
OptionalJacobian<2, FixedDimension<POINT>::value> Dpoint) const {
// We just call 3-derivative version in Base
Matrix26 Dpose;
Eigen::Matrix<double, 2, DimK> Dcal;
Point2 pi = Base::project(pw, Dcamera ? &Dpose : 0, Dpoint,
Dcamera ? &Dcal : 0);
if (Dcamera)
if (Dcamera){
Matrix26 Dpose;
Eigen::Matrix<double, 2, DimK> Dcal;
const Point2 pi = Base::project(pw, Dpose, Dpoint, Dcal);
*Dcamera << Dpose, Dcal;
return pi;
return pi;
} else {
return Base::project(pw, boost::none, Dpoint, boost::none);
}
}
/// project a 3D point from world coordinates into the image

2
gtsam/geometry/PinholePose.h
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@ -31,7 +31,7 @@ namespace gtsam {
* \nosubgrouping
*/
template<typename CALIBRATION>
class GTSAM_EXPORT PinholeBaseK: public PinholeBase {
class PinholeBaseK: public PinholeBase {
private:

1
gtsam/geometry/Point3.cpp
View File

@ -17,6 +17,7 @@
#include <gtsam/geometry/Point3.h>
#include <cmath>
#include <iostream>
#include <vector>
using namespace std;

98
gtsam/geometry/Pose2.cpp
View File

@ -213,6 +213,14 @@ Point2 Pose2::transformTo(const Point2& point,
return q;
}
Matrix Pose2::transformTo(const Matrix& points) const {
if (points.rows() != 2) {
throw std::invalid_argument("Pose2:transformTo expects 2*N matrix.");
}
const Matrix2 Rt = rotation().transpose();
return Rt * (points.colwise() - t_); // Eigen broadcasting!
}
/* ************************************************************************* */
// see doc/math.lyx, SE(2) section
Point2 Pose2::transformFrom(const Point2& point,
@ -224,6 +232,15 @@ Point2 Pose2::transformFrom(const Point2& point,
return q + t_;
}
Matrix Pose2::transformFrom(const Matrix& points) const {
if (points.rows() != 2) {
throw std::invalid_argument("Pose2:transformFrom expects 2*N matrix.");
}
const Matrix2 R = rotation().matrix();
return (R * points).colwise() + t_; // Eigen broadcasting!
}
/* ************************************************************************* */
Rot2 Pose2::bearing(const Point2& point,
OptionalJacobian<1, 3> Hpose, OptionalJacobian<1, 2> Hpoint) const {
@ -292,54 +309,77 @@ double Pose2::range(const Pose2& pose,
}
/* *************************************************************************
* New explanation, from scan.ml
* It finds the angle using a linear method:
* q = Pose2::transformFrom(p) = t + R*p
* Align finds the angle using a linear method:
* a = Pose2::transformFrom(b) = t + R*b
* We need to remove the centroids from the data to find the rotation
* using dp=[dpx;dpy] and q=[dqx;dqy] we have
* |dqx| |c -s| |dpx| |dpx -dpy| |c|
* using db=[dbx;dby] and a=[dax;day] we have
* |dax| |c -s| |dbx| |dbx -dby| |c|
* | | = | | * | | = | | * | | = H_i*cs
* |dqy| |s c| |dpy| |dpy dpx| |s|
* |day| |s c| |dby| |dby dbx| |s|
* where the Hi are the 2*2 matrices. Then we will minimize the criterion
* J = \sum_i norm(q_i - H_i * cs)
* J = \sum_i norm(a_i - H_i * cs)
* Taking the derivative with respect to cs and setting to zero we have
* cs = (\sum_i H_i' * q_i)/(\sum H_i'*H_i)
* cs = (\sum_i H_i' * a_i)/(\sum H_i'*H_i)
* The hessian is diagonal and just divides by a constant, but this
* normalization constant is irrelevant, since we take atan2.
* i.e., cos ~ sum(dpx*dqx + dpy*dqy) and sin ~ sum(-dpy*dqx + dpx*dqy)
* i.e., cos ~ sum(dbx*dax + dby*day) and sin ~ sum(-dby*dax + dbx*day)
* The translation is then found from the centroids
* as they also satisfy cq = t + R*cp, hence t = cq - R*cp
* as they also satisfy ca = t + R*cb, hence t = ca - R*cb
*/
boost::optional<Pose2> align(const vector<Point2Pair>& pairs) {
size_t n = pairs.size();
if (n<2) return boost::none; // we need at least two pairs
boost::optional<Pose2> Pose2::Align(const Point2Pairs &ab_pairs) {
const size_t n = ab_pairs.size();
if (n < 2) {
return boost::none; // we need at least 2 pairs
}
// calculate centroids
Point2 cp(0,0), cq(0,0);
for(const Point2Pair& pair: pairs) {
cp += pair.first;
cq += pair.second;
Point2 ca(0, 0), cb(0, 0);
for (const Point2Pair& pair : ab_pairs) {
ca += pair.first;
cb += pair.second;
}
double f = 1.0/n;
cp *= f; cq *= f;
const double f = 1.0/n;
ca *= f;
cb *= f;
// calculate cos and sin
double c=0,s=0;
for(const Point2Pair& pair: pairs) {
Point2 dp = pair.first - cp;
Point2 dq = pair.second - cq;
c += dp.x() * dq.x() + dp.y() * dq.y();
s += -dp.y() * dq.x() + dp.x() * dq.y();
double c = 0, s = 0;
for (const Point2Pair& pair : ab_pairs) {
Point2 da = pair.first - ca;
Point2 db = pair.second - cb;
c += db.x() * da.x() + db.y() * da.y();
s += -db.y() * da.x() + db.x() * da.y();
}
// calculate angle and translation
double theta = atan2(s,c);
Rot2 R = Rot2::fromAngle(theta);
Point2 t = cq - R*cp;
const double theta = atan2(s, c);
const Rot2 R = Rot2::fromAngle(theta);
const Point2 t = ca - R*cb;
return Pose2(R, t);
}
boost::optional<Pose2> Pose2::Align(const Matrix& a, const Matrix& b) {
if (a.rows() != 2 || b.rows() != 2 || a.cols() != b.cols()) {
throw std::invalid_argument(
"Pose2:Align expects 2*N matrices of equal shape.");
}
Point2Pairs ab_pairs;
for (size_t j=0; j < a.cols(); j++) {
ab_pairs.emplace_back(a.col(j), b.col(j));
}
return Pose2::Align(ab_pairs);
}
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V42
boost::optional<Pose2> align(const Point2Pairs& ba_pairs) {
Point2Pairs ab_pairs;
for (const Point2Pair &baPair : ba_pairs) {
ab_pairs.emplace_back(baPair.second, baPair.first);
}
return Pose2::Align(ab_pairs);
}
#endif
/* ************************************************************************* */
} // namespace gtsam

37
gtsam/geometry/Pose2.h
View File

@ -92,6 +92,18 @@ public:
*this = Expmap(v);
}
/**
* Create Pose2 by aligning two point pairs
* A pose aTb is estimated between pairs (a_point, b_point) such that
* a_point = aTb * b_point
* Note this allows for noise on the points but in that case the mapping
* will not be exact.
*/
static boost::optional<Pose2> Align(const Point2Pairs& abPointPairs);
// Version of Pose2::Align that takes 2 matrices.
static boost::optional<Pose2> Align(const Matrix& a, const Matrix& b);
/// @}
/// @name Testable
/// @{
@ -199,13 +211,29 @@ public:
OptionalJacobian<2, 3> Dpose = boost::none,
OptionalJacobian<2, 2> Dpoint = boost::none) const;
/**
* @brief transform many points in world coordinates and transform to Pose.
* @param points 2*N matrix in world coordinates
* @return points in Pose coordinates, as 2*N Matrix
*/
Matrix transformTo(const Matrix& points) const;
/** Return point coordinates in global frame */
GTSAM_EXPORT Point2 transformFrom(const Point2& point,
OptionalJacobian<2, 3> Dpose = boost::none,
OptionalJacobian<2, 2> Dpoint = boost::none) const;
/**
* @brief transform many points in Pose coordinates and transform to world.
* @param points 2*N matrix in Pose coordinates
* @return points in world coordinates, as 2*N Matrix
*/
Matrix transformFrom(const Matrix& points) const;
/** syntactic sugar for transformFrom */
inline Point2 operator*(const Point2& point) const { return transformFrom(point);}
inline Point2 operator*(const Point2& point) const {
return transformFrom(point);
}
/// @}
/// @name Standard Interface
@ -315,12 +343,15 @@ inline Matrix wedge<Pose2>(const Vector& xi) {
return Matrix(Pose2::wedge(xi(0),xi(1),xi(2))).eval();
}
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V42
/**
* @deprecated Use static constructor (with reversed pairs!)
* Calculate pose between a vector of 2D point correspondences (p,q)
* where q = Pose2::transformFrom(p) = t + R*p
*/
typedef std::pair<Point2,Point2> Point2Pair;
GTSAM_EXPORT boost::optional<Pose2> align(const std::vector<Point2Pair>& pairs);
GTSAM_EXPORT boost::optional<Pose2>
GTSAM_DEPRECATED align(const Point2Pairs& pairs);
#endif
template <>
struct traits<Pose2> : public internal::LieGroup<Pose2> {};

34
gtsam/geometry/Pose3.cpp
View File

@ -59,7 +59,7 @@ Matrix6 Pose3::AdjointMap() const {
const Matrix3 R = R_.matrix();
Matrix3 A = skewSymmetric(t_.x(), t_.y(), t_.z()) * R;
Matrix6 adj;
adj << R, Z_3x3, A, R;
adj << R, Z_3x3, A, R; // Gives [R 0; A R]
return adj;
}
@ -354,6 +354,14 @@ Point3 Pose3::transformFrom(const Point3& point, OptionalJacobian<3, 6> Hself,
return R_ * point + t_;
}
Matrix Pose3::transformFrom(const Matrix& points) const {
if (points.rows() != 3) {
throw std::invalid_argument("Pose3:transformFrom expects 3*N matrix.");
}
const Matrix3 R = R_.matrix();
return (R * points).colwise() + t_; // Eigen broadcasting!
}
/* ************************************************************************* */
Point3 Pose3::transformTo(const Point3& point, OptionalJacobian<3, 6> Hself,
OptionalJacobian<3, 3> Hpoint) const {
@ -374,6 +382,14 @@ Point3 Pose3::transformTo(const Point3& point, OptionalJacobian<3, 6> Hself,
return q;
}
Matrix Pose3::transformTo(const Matrix& points) const {
if (points.rows() != 3) {
throw std::invalid_argument("Pose3:transformTo expects 3*N matrix.");
}
const Matrix3 Rt = R_.transpose();
return Rt * (points.colwise() - t_); // Eigen broadcasting!
}
/* ************************************************************************* */
double Pose3::range(const Point3& point, OptionalJacobian<1, 6> Hself,
OptionalJacobian<1, 3> Hpoint) const {
@ -431,7 +447,7 @@ Unit3 Pose3::bearing(const Pose3& pose, OptionalJacobian<2, 6> Hself,
boost::optional<Pose3> Pose3::Align(const Point3Pairs &abPointPairs) {
const size_t n = abPointPairs.size();
if (n < 3) {
return boost::none; // we need at least three pairs
return boost::none; // we need at least three pairs
}
// calculate centroids
@ -451,6 +467,19 @@ boost::optional<Pose3> Pose3::Align(const Point3Pairs &abPointPairs) {
return Pose3(aRb, aTb);
}
boost::optional<Pose3> Pose3::Align(const Matrix& a, const Matrix& b) {
if (a.rows() != 3 || b.rows() != 3 || a.cols() != b.cols()) {
throw std::invalid_argument(
"Pose3:Align expects 3*N matrices of equal shape.");
}
Point3Pairs abPointPairs;
for (size_t j=0; j < a.cols(); j++) {
abPointPairs.emplace_back(a.col(j), b.col(j));
}
return Pose3::Align(abPointPairs);
}
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V42
boost::optional<Pose3> align(const Point3Pairs &baPointPairs) {
Point3Pairs abPointPairs;
for (const Point3Pair &baPair : baPointPairs) {
@ -458,6 +487,7 @@ boost::optional<Pose3> align(const Point3Pairs &baPointPairs) {
}
return Pose3::Align(abPointPairs);
}
#endif
/* ************************************************************************* */
std::ostream &operator<<(std::ostream &os, const Pose3& pose) {

17
gtsam/geometry/Pose3.h
View File

@ -85,6 +85,9 @@ public:
*/
static boost::optional<Pose3> Align(const std::vector<Point3Pair>& abPointPairs);
// Version of Pose3::Align that takes 2 matrices.
static boost::optional<Pose3> Align(const Matrix& a, const Matrix& b);
/// @}
/// @name Testable
/// @{
@ -249,6 +252,13 @@ public:
Point3 transformFrom(const Point3& point, OptionalJacobian<3, 6> Hself =
boost::none, OptionalJacobian<3, 3> Hpoint = boost::none) const;
/**
* @brief transform many points in Pose coordinates and transform to world.
* @param points 3*N matrix in Pose coordinates
* @return points in world coordinates, as 3*N Matrix
*/
Matrix transformFrom(const Matrix& points) const;
/** syntactic sugar for transformFrom */
inline Point3 operator*(const Point3& point) const {
return transformFrom(point);
@ -264,6 +274,13 @@ public:
Point3 transformTo(const Point3& point, OptionalJacobian<3, 6> Hself =
boost::none, OptionalJacobian<3, 3> Hpoint = boost::none) const;
/**
* @brief transform many points in world coordinates and transform to Pose.
* @param points 3*N matrix in world coordinates
* @return points in Pose coordinates, as 3*N Matrix
*/
Matrix transformTo(const Matrix& points) const;
/// @}
/// @name Standard Interface
/// @{

9
gtsam/geometry/SOn.cpp
View File

@ -22,7 +22,7 @@
namespace gtsam {
template <>
GTSAM_EXPORT void SOn::Hat(const Vector &xi, Eigen::Ref<Matrix> X) {
void SOn::Hat(const Vector &xi, Eigen::Ref<Matrix> X) {
size_t n = AmbientDim(xi.size());
if (n < 2)
throw std::invalid_argument("SO<N>::Hat: n<2 not supported");
@ -48,7 +48,7 @@ GTSAM_EXPORT void SOn::Hat(const Vector &xi, Eigen::Ref<Matrix> X) {
}
}
template <> GTSAM_EXPORT Matrix SOn::Hat(const Vector &xi) {
template <> Matrix SOn::Hat(const Vector &xi) {
size_t n = AmbientDim(xi.size());
Matrix X(n, n); // allocate space for n*n skew-symmetric matrix
SOn::Hat(xi, X);
@ -56,7 +56,6 @@ template <> GTSAM_EXPORT Matrix SOn::Hat(const Vector &xi) {
}
template <>
GTSAM_EXPORT
Vector SOn::Vee(const Matrix& X) {
const size_t n = X.rows();
if (n < 2) throw std::invalid_argument("SO<N>::Hat: n<2 not supported");
@ -104,7 +103,9 @@ SOn LieGroup<SOn, Eigen::Dynamic>::between(const SOn& g, DynamicJacobian H1,
}
// Dynamic version of vec
template <> typename SOn::VectorN2 SOn::vec(DynamicJacobian H) const {
template <>
typename SOn::VectorN2 SOn::vec(DynamicJacobian H) const
{
const size_t n = rows(), n2 = n * n;
// Vectorize

8
gtsam/geometry/SOn.h
View File

@ -24,6 +24,8 @@
#include <gtsam/dllexport.h>
#include <Eigen/Core>
#include <boost/serialization/nvp.hpp>
#include <iostream> // TODO(frank): how to avoid?
#include <string>
#include <type_traits>
@ -356,17 +358,21 @@ Vector SOn::Vee(const Matrix& X);
using DynamicJacobian = OptionalJacobian<Eigen::Dynamic, Eigen::Dynamic>;
template <>
GTSAM_EXPORT
SOn LieGroup<SOn, Eigen::Dynamic>::compose(const SOn& g, DynamicJacobian H1,
DynamicJacobian H2) const;
template <>
GTSAM_EXPORT
SOn LieGroup<SOn, Eigen::Dynamic>::between(const SOn& g, DynamicJacobian H1,
DynamicJacobian H2) const;
/*
* Specialize dynamic vec.
*/
template <> typename SOn::VectorN2 SOn::vec(DynamicJacobian H) const;
template <>
GTSAM_EXPORT
typename SOn::VectorN2 SOn::vec(DynamicJacobian H) const;
/** Serialization function */
template<class Archive>

31
gtsam/geometry/Unit3.h
View File

@ -23,11 +23,12 @@
#include <gtsam/geometry/Point2.h>
#include <gtsam/geometry/Point3.h>
#include <gtsam/base/Manifold.h>
#include <gtsam/base/Vector.h>
#include <gtsam/base/VectorSerialization.h>
#include <gtsam/base/Matrix.h>
#include <gtsam/dllexport.h>
#include <boost/optional.hpp>
#include <boost/serialization/nvp.hpp>
#include <random>
#include <string>
@ -39,7 +40,7 @@
namespace gtsam {
/// Represents a 3D point on a unit sphere.
class Unit3 {
class GTSAM_EXPORT Unit3 {
private:
@ -96,7 +97,7 @@ public:
}
/// Named constructor from Point3 with optional Jacobian
GTSAM_EXPORT static Unit3 FromPoint3(const Point3& point, //
static Unit3 FromPoint3(const Point3& point, //
OptionalJacobian<2, 3> H = boost::none);
/**
@ -105,7 +106,7 @@ public:
* std::mt19937 engine(42);
* Unit3 unit = Unit3::Random(engine);
*/
GTSAM_EXPORT static Unit3 Random(std::mt19937 & rng);
static Unit3 Random(std::mt19937 & rng);
/// @}
@ -115,7 +116,7 @@ public:
friend std::ostream& operator<<(std::ostream& os, const Unit3& pair);
/// The print fuction
GTSAM_EXPORT void print(const std::string& s = std::string()) const;
void print(const std::string& s = std::string()) const;
/// The equals function with tolerance
bool equals(const Unit3& s, double tol = 1e-9) const {
@ -132,16 +133,16 @@ public:
* tangent to the sphere at the current direction.
* Provides derivatives of the basis with the two basis vectors stacked up as a 6x1.
*/
GTSAM_EXPORT const Matrix32& basis(OptionalJacobian<6, 2> H = boost::none) const;
const Matrix32& basis(OptionalJacobian<6, 2> H = boost::none) const;
/// Return skew-symmetric associated with 3D point on unit sphere
GTSAM_EXPORT Matrix3 skew() const;
Matrix3 skew() const;
/// Return unit-norm Point3
GTSAM_EXPORT Point3 point3(OptionalJacobian<3, 2> H = boost::none) const;
Point3 point3(OptionalJacobian<3, 2> H = boost::none) const;
/// Return unit-norm Vector
GTSAM_EXPORT Vector3 unitVector(OptionalJacobian<3, 2> H = boost::none) const;
Vector3 unitVector(OptionalJacobian<3, 2> H = boost::none) const;
/// Return scaled direction as Point3
friend Point3 operator*(double s, const Unit3& d) {
@ -149,20 +150,20 @@ public:
}
/// Return dot product with q
GTSAM_EXPORT double dot(const Unit3& q, OptionalJacobian<1,2> H1 = boost::none, //
double dot(const Unit3& q, OptionalJacobian<1,2> H1 = boost::none, //
OptionalJacobian<1,2> H2 = boost::none) const;
/// Signed, vector-valued error between two directions
/// @deprecated, errorVector has the proper derivatives, this confusingly has only the second.
GTSAM_EXPORT Vector2 error(const Unit3& q, OptionalJacobian<2, 2> H_q = boost::none) const;
Vector2 error(const Unit3& q, OptionalJacobian<2, 2> H_q = boost::none) const;
/// Signed, vector-valued error between two directions
/// NOTE(hayk): This method has zero derivatives if this (p) and q are orthogonal.
GTSAM_EXPORT Vector2 errorVector(const Unit3& q, OptionalJacobian<2, 2> H_p = boost::none, //
Vector2 errorVector(const Unit3& q, OptionalJacobian<2, 2> H_p = boost::none, //
OptionalJacobian<2, 2> H_q = boost::none) const;
/// Distance between two directions
GTSAM_EXPORT double distance(const Unit3& q, OptionalJacobian<1, 2> H = boost::none) const;
double distance(const Unit3& q, OptionalJacobian<1, 2> H = boost::none) const;
/// Cross-product between two Unit3s
Unit3 cross(const Unit3& q) const {
@ -195,10 +196,10 @@ public:
};
/// The retract function
GTSAM_EXPORT Unit3 retract(const Vector2& v, OptionalJacobian<2,2> H = boost::none) const;
Unit3 retract(const Vector2& v, OptionalJacobian<2,2> H = boost::none) const;
/// The local coordinates function
GTSAM_EXPORT Vector2 localCoordinates(const Unit3& s) const;
Vector2 localCoordinates(const Unit3& s) const;
/// @}

84
gtsam/geometry/geometry.i
View File

@ -372,6 +372,9 @@ class Pose2 {
Pose2(const gtsam::Rot2& r, const gtsam::Point2& t);
Pose2(Vector v);
static boost::optional<gtsam::Pose2> Align(const gtsam::Point2Pairs& abPointPairs);
static boost::optional<gtsam::Pose2> Align(const gtsam::Matrix& a, const gtsam::Matrix& b);
// Testable
void print(string s = "") const;
bool equals(const gtsam::Pose2& pose, double tol) const;
@ -406,6 +409,10 @@ class Pose2 {
gtsam::Point2 transformFrom(const gtsam::Point2& p) const;
gtsam::Point2 transformTo(const gtsam::Point2& p) const;
// Matrix versions
Matrix transformFrom(const Matrix& points) const;
Matrix transformTo(const Matrix& points) const;
// Standard Interface
double x() const;
double y() const;
@ -420,8 +427,6 @@ class Pose2 {
void serialize() const;
};
boost::optional<gtsam::Pose2> align(const gtsam::Point2Pairs& pairs);
#include <gtsam/geometry/Pose3.h>
class Pose3 {
// Standard Constructors
@ -431,6 +436,9 @@ class Pose3 {
Pose3(const gtsam::Pose2& pose2);
Pose3(Matrix mat);
static boost::optional<gtsam::Pose3> Align(const gtsam::Point3Pairs& abPointPairs);
static boost::optional<gtsam::Pose3> Align(const gtsam::Matrix& a, const gtsam::Matrix& b);
// Testable
void print(string s = "") const;
bool equals(const gtsam::Pose3& pose, double tol) const;
@ -470,6 +478,10 @@ class Pose3 {
gtsam::Point3 transformFrom(const gtsam::Point3& point) const;
gtsam::Point3 transformTo(const gtsam::Point3& point) const;
// Matrix versions
Matrix transformFrom(const Matrix& points) const;
Matrix transformTo(const Matrix& points) const;
// Standard Interface
gtsam::Rot3 rotation() const;
gtsam::Point3 translation() const;
@ -572,7 +584,13 @@ class Cal3_S2 {
// Action on Point2
gtsam::Point2 calibrate(const gtsam::Point2& p) const;
gtsam::Point2 calibrate(const gtsam::Point2& p,
Eigen::Ref<Eigen::MatrixXd> Dcal,
Eigen::Ref<Eigen::MatrixXd> Dp) const;
gtsam::Point2 uncalibrate(const gtsam::Point2& p) const;
gtsam::Point2 uncalibrate(const gtsam::Point2& p,
Eigen::Ref<Eigen::MatrixXd> Dcal,
Eigen::Ref<Eigen::MatrixXd> Dp) const;
// Standard Interface
double fx() const;
@ -611,7 +629,13 @@ virtual class Cal3DS2_Base {
// Action on Point2
gtsam::Point2 uncalibrate(const gtsam::Point2& p) const;
gtsam::Point2 uncalibrate(const gtsam::Point2& p,
Eigen::Ref<Eigen::MatrixXd> Dcal,
Eigen::Ref<Eigen::MatrixXd> Dp) const;
gtsam::Point2 calibrate(const gtsam::Point2& p) const;
gtsam::Point2 calibrate(const gtsam::Point2& p,
Eigen::Ref<Eigen::MatrixXd> Dcal,
Eigen::Ref<Eigen::MatrixXd> Dp) const;
// enabling serialization functionality
void serialize() const;
@ -668,7 +692,13 @@ virtual class Cal3Unified : gtsam::Cal3DS2_Base {
// Note: the signature of this functions differ from the functions
// with equal name in the base class.
gtsam::Point2 calibrate(const gtsam::Point2& p) const;
gtsam::Point2 calibrate(const gtsam::Point2& p,
Eigen::Ref<Eigen::MatrixXd> Dcal,
Eigen::Ref<Eigen::MatrixXd> Dp) const;
gtsam::Point2 uncalibrate(const gtsam::Point2& p) const;
gtsam::Point2 uncalibrate(const gtsam::Point2& p,
Eigen::Ref<Eigen::MatrixXd> Dcal,
Eigen::Ref<Eigen::MatrixXd> Dp) const;
// enabling serialization functionality
void serialize() const;
@ -694,7 +724,13 @@ class Cal3Fisheye {
// Action on Point2
gtsam::Point2 calibrate(const gtsam::Point2& p) const;
gtsam::Point2 calibrate(const gtsam::Point2& p,
Eigen::Ref<Eigen::MatrixXd> Dcal,
Eigen::Ref<Eigen::MatrixXd> Dp) const;
gtsam::Point2 uncalibrate(const gtsam::Point2& p) const;
gtsam::Point2 uncalibrate(const gtsam::Point2& p,
Eigen::Ref<Eigen::MatrixXd> Dcal,
Eigen::Ref<Eigen::MatrixXd> Dp) const;
// Standard Interface
double fx() const;
@ -757,7 +793,13 @@ class Cal3Bundler {
// Action on Point2
gtsam::Point2 calibrate(const gtsam::Point2& p) const;
gtsam::Point2 calibrate(const gtsam::Point2& p,
Eigen::Ref<Eigen::MatrixXd> Dcal,
Eigen::Ref<Eigen::MatrixXd> Dp) const;
gtsam::Point2 uncalibrate(const gtsam::Point2& p) const;
gtsam::Point2 uncalibrate(const gtsam::Point2& p,
Eigen::Ref<Eigen::MatrixXd> Dcal,
Eigen::Ref<Eigen::MatrixXd> Dp) const;
// Standard Interface
double fx() const;
@ -795,12 +837,25 @@ class CalibratedCamera {
// Action on Point3
gtsam::Point2 project(const gtsam::Point3& point) const;
gtsam::Point2 project(const gtsam::Point3& point,
Eigen::Ref<Eigen::MatrixXd> Dcamera,
Eigen::Ref<Eigen::MatrixXd> Dpoint);
gtsam::Point3 backproject(const gtsam::Point2& p, double depth) const;
gtsam::Point3 backproject(const gtsam::Point2& p, double depth,
Eigen::Ref<Eigen::MatrixXd> Dresult_dpose,
Eigen::Ref<Eigen::MatrixXd> Dresult_dp,
Eigen::Ref<Eigen::MatrixXd> Dresult_ddepth);
static gtsam::Point2 Project(const gtsam::Point3& cameraPoint);
// Standard Interface
gtsam::Pose3 pose() const;
double range(const gtsam::Point3& point) const;
double range(const gtsam::Point3& point, Eigen::Ref<Eigen::MatrixXd> Dcamera,
Eigen::Ref<Eigen::MatrixXd> Dpoint);
double range(const gtsam::Pose3& pose) const;
double range(const gtsam::Pose3& point, Eigen::Ref<Eigen::MatrixXd> Dcamera,
Eigen::Ref<Eigen::MatrixXd> Dpose);
double range(const gtsam::CalibratedCamera& camera) const;
// enabling serialization functionality
@ -812,6 +867,7 @@ template <CALIBRATION>
class PinholeCamera {
// Standard Constructors and Named Constructors
PinholeCamera();
PinholeCamera(const gtsam::PinholeCamera<CALIBRATION> other);
PinholeCamera(const gtsam::Pose3& pose);
PinholeCamera(const gtsam::Pose3& pose, const CALIBRATION& K);
static This Level(const CALIBRATION& K, const gtsam::Pose2& pose,
@ -838,9 +894,22 @@ class PinholeCamera {
static gtsam::Point2 Project(const gtsam::Point3& cameraPoint);
pair<gtsam::Point2, bool> projectSafe(const gtsam::Point3& pw) const;
gtsam::Point2 project(const gtsam::Point3& point);
gtsam::Point2 project(const gtsam::Point3& point,
Eigen::Ref<Eigen::MatrixXd> Dpose,
Eigen::Ref<Eigen::MatrixXd> Dpoint,
Eigen::Ref<Eigen::MatrixXd> Dcal);
gtsam::Point3 backproject(const gtsam::Point2& p, double depth) const;
gtsam::Point3 backproject(const gtsam::Point2& p, double depth,
Eigen::Ref<Eigen::MatrixXd> Dresult_dpose,
Eigen::Ref<Eigen::MatrixXd> Dresult_dp,
Eigen::Ref<Eigen::MatrixXd> Dresult_ddepth,
Eigen::Ref<Eigen::MatrixXd> Dresult_dcal);
double range(const gtsam::Point3& point);
double range(const gtsam::Point3& point, Eigen::Ref<Eigen::MatrixXd> Dcamera,
Eigen::Ref<Eigen::MatrixXd> Dpoint);
double range(const gtsam::Pose3& pose);
double range(const gtsam::Pose3& pose, Eigen::Ref<Eigen::MatrixXd> Dcamera,
Eigen::Ref<Eigen::MatrixXd> Dpose);
// enabling serialization functionality
void serialize() const;
@ -909,9 +978,18 @@ class StereoCamera {
static size_t Dim();
// Transformations and measurement functions
gtsam::StereoPoint2 project(const gtsam::Point3& point);
gtsam::StereoPoint2 project(const gtsam::Point3& point) const;
gtsam::Point3 backproject(const gtsam::StereoPoint2& p) const;
// project with Jacobian
gtsam::StereoPoint2 project2(const gtsam::Point3& point,
Eigen::Ref<Eigen::MatrixXd> H1,
Eigen::Ref<Eigen::MatrixXd> H2) const;
gtsam::Point3 backproject2(const gtsam::StereoPoint2& p,
Eigen::Ref<Eigen::MatrixXd> H1,
Eigen::Ref<Eigen::MatrixXd> H2) const;
// enabling serialization functionality
void serialize() const;
};

2
gtsam/geometry/tests/testCal3Bundler.cpp
View File

@ -160,7 +160,7 @@ TEST(Cal3Bundler, retract) {
}
/* ************************************************************************* */
TEST(Cal3_S2, Print) {
TEST(Cal3Bundler, Print) {
Cal3Bundler cal(1, 2, 3, 4, 5);
std::stringstream os;
os << "f: " << cal.fx() << ", k1: " << cal.k1() << ", k2: " << cal.k2()

121
gtsam/geometry/tests/testPose2.cpp
View File

@ -717,123 +717,112 @@ TEST( Pose2, range_pose )
/* ************************************************************************* */
TEST(Pose2, align_1) {
Pose2 expected(Rot2::fromAngle(0), Point2(10,10));
vector<Point2Pair> correspondences;
Point2Pair pq1(make_pair(Point2(0,0), Point2(10,10)));
Point2Pair pq2(make_pair(Point2(20,10), Point2(30,20)));
correspondences += pq1, pq2;
boost::optional<Pose2> actual = align(correspondences);
EXPECT(assert_equal(expected, *actual));
Pose2 expected(Rot2::fromAngle(0), Point2(10, 10));
Point2Pairs ab_pairs {{Point2(10, 10), Point2(0, 0)},
{Point2(30, 20), Point2(20, 10)}};
boost::optional<Pose2> aTb = Pose2::Align(ab_pairs);
EXPECT(assert_equal(expected, *aTb));
}
TEST(Pose2, align_2) {
Point2 t(20,10);
Point2 t(20, 10);
Rot2 R = Rot2::fromAngle(M_PI/2.0);
Pose2 expected(R, t);
vector<Point2Pair> correspondences;
Point2 p1(0,0), p2(10,0);
Point2 q1 = expected.transformFrom(p1), q2 = expected.transformFrom(p2);
EXPECT(assert_equal(Point2(20,10),q1));
EXPECT(assert_equal(Point2(20,20),q2));
Point2Pair pq1(make_pair(p1, q1));
Point2Pair pq2(make_pair(p2, q2));
correspondences += pq1, pq2;
Point2 b1(0, 0), b2(10, 0);
Point2Pairs ab_pairs {{expected.transformFrom(b1), b1},
{expected.transformFrom(b2), b2}};
boost::optional<Pose2> actual = align(correspondences);
EXPECT(assert_equal(expected, *actual));
boost::optional<Pose2> aTb = Pose2::Align(ab_pairs);
EXPECT(assert_equal(expected, *aTb));
}
namespace align_3 {
Point2 t(10,10);
Point2 t(10, 10);
Pose2 expected(Rot2::fromAngle(2*M_PI/3), t);
Point2 p1(0,0), p2(10,0), p3(10,10);
Point2 q1 = expected.transformFrom(p1), q2 = expected.transformFrom(p2), q3 = expected.transformFrom(p3);
Point2 b1(0, 0), b2(10, 0), b3(10, 10);
Point2 a1 = expected.transformFrom(b1),
a2 = expected.transformFrom(b2),
a3 = expected.transformFrom(b3);
}
TEST(Pose2, align_3) {
using namespace align_3;
vector<Point2Pair> correspondences;
Point2Pair pq1(make_pair(p1, q1));
Point2Pair pq2(make_pair(p2, q2));
Point2Pair pq3(make_pair(p3, q3));
correspondences += pq1, pq2, pq3;
Point2Pairs ab_pairs;
Point2Pair ab1(make_pair(a1, b1));
Point2Pair ab2(make_pair(a2, b2));
Point2Pair ab3(make_pair(a3, b3));
ab_pairs += ab1, ab2, ab3;
boost::optional<Pose2> actual = align(correspondences);
EXPECT(assert_equal(expected, *actual));
boost::optional<Pose2> aTb = Pose2::Align(ab_pairs);
EXPECT(assert_equal(expected, *aTb));
}
namespace {
/* ************************************************************************* */
// Prototype code to align two triangles using a rigid transform
/* ************************************************************************* */
struct Triangle { size_t i_,j_,k_;};
struct Triangle { size_t i_, j_, k_;};
boost::optional<Pose2> align2(const Point2Vector& ps, const Point2Vector& qs,
boost::optional<Pose2> align2(const Point2Vector& as, const Point2Vector& bs,
const pair<Triangle, Triangle>& trianglePair) {
const Triangle& t1 = trianglePair.first, t2 = trianglePair.second;
vector<Point2Pair> correspondences;
correspondences += make_pair(ps[t1.i_],qs[t2.i_]), make_pair(ps[t1.j_],qs[t2.j_]), make_pair(ps[t1.k_],qs[t2.k_]);
return align(correspondences);
Point2Pairs ab_pairs = {{as[t1.i_], bs[t2.i_]},
{as[t1.j_], bs[t2.j_]},
{as[t1.k_], bs[t2.k_]}};
return Pose2::Align(ab_pairs);
}
}
TEST(Pose2, align_4) {
using namespace align_3;
Point2Vector ps,qs;
ps += p1, p2, p3;
qs += q3, q1, q2; // note in 3,1,2 order !
Point2Vector as, bs;
as += a1, a2, a3;
bs += b3, b1, b2; // note in 3,1,2 order !
Triangle t1; t1.i_=0; t1.j_=1; t1.k_=2;
Triangle t2; t2.i_=1; t2.j_=2; t2.k_=0;
boost::optional<Pose2> actual = align2(ps, qs, make_pair(t1,t2));
boost::optional<Pose2> actual = align2(as, bs, {t1, t2});
EXPECT(assert_equal(expected, *actual));
}
//******************************************************************************
namespace {
Pose2 id;
Pose2 T1(M_PI / 4.0, Point2(sqrt(0.5), sqrt(0.5)));
Pose2 T2(M_PI / 2.0, Point2(0.0, 2.0));
} // namespace
//******************************************************************************
TEST(Pose2 , Invariants) {
Pose2 id;
EXPECT(check_group_invariants(id,id));
EXPECT(check_group_invariants(id,T1));
EXPECT(check_group_invariants(T2,id));
EXPECT(check_group_invariants(T2,T1));
EXPECT(check_manifold_invariants(id,id));
EXPECT(check_manifold_invariants(id,T1));
EXPECT(check_manifold_invariants(T2,id));
EXPECT(check_manifold_invariants(T2,T1));
TEST(Pose2, Invariants) {
EXPECT(check_group_invariants(id, id));
EXPECT(check_group_invariants(id, T1));
EXPECT(check_group_invariants(T2, id));
EXPECT(check_group_invariants(T2, T1));
EXPECT(check_manifold_invariants(id, id));
EXPECT(check_manifold_invariants(id, T1));
EXPECT(check_manifold_invariants(T2, id));
EXPECT(check_manifold_invariants(T2, T1));
}
//******************************************************************************
TEST(Pose2 , LieGroupDerivatives) {
Pose2 id;
CHECK_LIE_GROUP_DERIVATIVES(id,id);
CHECK_LIE_GROUP_DERIVATIVES(id,T2);
CHECK_LIE_GROUP_DERIVATIVES(T2,id);
CHECK_LIE_GROUP_DERIVATIVES(T2,T1);
TEST(Pose2, LieGroupDerivatives) {
CHECK_LIE_GROUP_DERIVATIVES(id, id);
CHECK_LIE_GROUP_DERIVATIVES(id, T2);
CHECK_LIE_GROUP_DERIVATIVES(T2, id);
CHECK_LIE_GROUP_DERIVATIVES(T2, T1);
}
//******************************************************************************
TEST(Pose2 , ChartDerivatives) {
Pose2 id;
CHECK_CHART_DERIVATIVES(id,id);
CHECK_CHART_DERIVATIVES(id,T2);
CHECK_CHART_DERIVATIVES(T2,id);
CHECK_CHART_DERIVATIVES(T2,T1);
TEST(Pose2, ChartDerivatives) {
CHECK_CHART_DERIVATIVES(id, id);
CHECK_CHART_DERIVATIVES(id, T2);
CHECK_CHART_DERIVATIVES(T2, id);
CHECK_CHART_DERIVATIVES(T2, T1);
}
//******************************************************************************

20
gtsam/geometry/tests/testQuaternion.cpp
View File

@ -80,12 +80,6 @@ TEST(Quaternion , Compose) {
EXPECT(traits<Q>::Equals(expected, actual));
}
//******************************************************************************
Vector3 Q_z_axis(0, 0, 1);
Q id(Eigen::AngleAxisd(0, Q_z_axis));
Q R1(Eigen::AngleAxisd(1, Q_z_axis));
Q R2(Eigen::AngleAxisd(2, Vector3(0, 1, 0)));
//******************************************************************************
TEST(Quaternion , Between) {
Vector3 z_axis(0, 0, 1);
@ -108,7 +102,15 @@ TEST(Quaternion , Inverse) {
}
//******************************************************************************
TEST(Quaternion , Invariants) {
namespace {
Vector3 Q_z_axis(0, 0, 1);
Q id(Eigen::AngleAxisd(0, Q_z_axis));
Q R1(Eigen::AngleAxisd(1, Q_z_axis));
Q R2(Eigen::AngleAxisd(2, Vector3(0, 1, 0)));
} // namespace
//******************************************************************************
TEST(Quaternion, Invariants) {
EXPECT(check_group_invariants(id, id));
EXPECT(check_group_invariants(id, R1));
EXPECT(check_group_invariants(R2, id));
@ -121,7 +123,7 @@ TEST(Quaternion , Invariants) {
}
//******************************************************************************
TEST(Quaternion , LieGroupDerivatives) {
TEST(Quaternion, LieGroupDerivatives) {
CHECK_LIE_GROUP_DERIVATIVES(id, id);
CHECK_LIE_GROUP_DERIVATIVES(id, R2);
CHECK_LIE_GROUP_DERIVATIVES(R2, id);
@ -129,7 +131,7 @@ TEST(Quaternion , LieGroupDerivatives) {
}
//******************************************************************************
TEST(Quaternion , ChartDerivatives) {
TEST(Quaternion, ChartDerivatives) {
CHECK_CHART_DERIVATIVES(id, id);
CHECK_CHART_DERIVATIVES(id, R2);
CHECK_CHART_DERIVATIVES(R2, id);

49
gtsam/geometry/tests/testRot2.cpp
View File

@ -156,44 +156,39 @@ TEST( Rot2, relativeBearing )
}
//******************************************************************************
namespace {
Rot2 id;
Rot2 T1(0.1);
Rot2 T2(0.2);
} // namespace
//******************************************************************************
TEST(Rot2 , Invariants) {
Rot2 id;
EXPECT(check_group_invariants(id,id));
EXPECT(check_group_invariants(id,T1));
EXPECT(check_group_invariants(T2,id));
EXPECT(check_group_invariants(T2,T1));
EXPECT(check_manifold_invariants(id,id));
EXPECT(check_manifold_invariants(id,T1));
EXPECT(check_manifold_invariants(T2,id));
EXPECT(check_manifold_invariants(T2,T1));
TEST(Rot2, Invariants) {
EXPECT(check_group_invariants(id, id));
EXPECT(check_group_invariants(id, T1));
EXPECT(check_group_invariants(T2, id));
EXPECT(check_group_invariants(T2, T1));
EXPECT(check_manifold_invariants(id, id));
EXPECT(check_manifold_invariants(id, T1));
EXPECT(check_manifold_invariants(T2, id));
EXPECT(check_manifold_invariants(T2, T1));
}
//******************************************************************************
TEST(Rot2 , LieGroupDerivatives) {
Rot2 id;
CHECK_LIE_GROUP_DERIVATIVES(id,id);
CHECK_LIE_GROUP_DERIVATIVES(id,T2);
CHECK_LIE_GROUP_DERIVATIVES(T2,id);
CHECK_LIE_GROUP_DERIVATIVES(T2,T1);
TEST(Rot2, LieGroupDerivatives) {
CHECK_LIE_GROUP_DERIVATIVES(id, id);
CHECK_LIE_GROUP_DERIVATIVES(id, T2);
CHECK_LIE_GROUP_DERIVATIVES(T2, id);
CHECK_LIE_GROUP_DERIVATIVES(T2, T1);
}
//******************************************************************************
TEST(Rot2 , ChartDerivatives) {
Rot2 id;
CHECK_CHART_DERIVATIVES(id,id);
CHECK_CHART_DERIVATIVES(id,T2);
CHECK_CHART_DERIVATIVES(T2,id);
CHECK_CHART_DERIVATIVES(T2,T1);
TEST(Rot2, ChartDerivatives) {
CHECK_CHART_DERIVATIVES(id, id);
CHECK_CHART_DERIVATIVES(id, T2);
CHECK_CHART_DERIVATIVES(T2, id);
CHECK_CHART_DERIVATIVES(T2, T1);
}
/* ************************************************************************* */

54
gtsam/geometry/tests/testRot3.cpp
View File

@ -640,46 +640,44 @@ TEST( Rot3, slerp)
}
//******************************************************************************
namespace {
Rot3 id;
Rot3 T1(Rot3::AxisAngle(Vector3(0, 0, 1), 1));
Rot3 T2(Rot3::AxisAngle(Vector3(0, 1, 0), 2));
} // namespace
//******************************************************************************
TEST(Rot3 , Invariants) {
Rot3 id;
TEST(Rot3, Invariants) {
EXPECT(check_group_invariants(id, id));
EXPECT(check_group_invariants(id, T1));
EXPECT(check_group_invariants(T2, id));
EXPECT(check_group_invariants(T2, T1));
EXPECT(check_group_invariants(T1, T2));
EXPECT(check_group_invariants(id,id));
EXPECT(check_group_invariants(id,T1));
EXPECT(check_group_invariants(T2,id));
EXPECT(check_group_invariants(T2,T1));
EXPECT(check_group_invariants(T1,T2));
EXPECT(check_manifold_invariants(id,id));
EXPECT(check_manifold_invariants(id,T1));
EXPECT(check_manifold_invariants(T2,id));
EXPECT(check_manifold_invariants(T2,T1));
EXPECT(check_manifold_invariants(T1,T2));
EXPECT(check_manifold_invariants(id, id));
EXPECT(check_manifold_invariants(id, T1));
EXPECT(check_manifold_invariants(T2, id));
EXPECT(check_manifold_invariants(T2, T1));
EXPECT(check_manifold_invariants(T1, T2));
}
//******************************************************************************
TEST(Rot3 , LieGroupDerivatives) {
Rot3 id;
CHECK_LIE_GROUP_DERIVATIVES(id,id);
CHECK_LIE_GROUP_DERIVATIVES(id,T2);
CHECK_LIE_GROUP_DERIVATIVES(T2,id);
CHECK_LIE_GROUP_DERIVATIVES(T1,T2);
CHECK_LIE_GROUP_DERIVATIVES(T2,T1);
TEST(Rot3, LieGroupDerivatives) {
CHECK_LIE_GROUP_DERIVATIVES(id, id);
CHECK_LIE_GROUP_DERIVATIVES(id, T2);
CHECK_LIE_GROUP_DERIVATIVES(T2, id);
CHECK_LIE_GROUP_DERIVATIVES(T1, T2);
CHECK_LIE_GROUP_DERIVATIVES(T2, T1);
}
//******************************************************************************
TEST(Rot3 , ChartDerivatives) {
Rot3 id;
TEST(Rot3, ChartDerivatives) {
if (ROT3_DEFAULT_COORDINATES_MODE == Rot3::EXPMAP) {
CHECK_CHART_DERIVATIVES(id,id);
CHECK_CHART_DERIVATIVES(id,T2);
CHECK_CHART_DERIVATIVES(T2,id);
CHECK_CHART_DERIVATIVES(T1,T2);
CHECK_CHART_DERIVATIVES(T2,T1);
CHECK_CHART_DERIVATIVES(id, id);
CHECK_CHART_DERIVATIVES(id, T2);
CHECK_CHART_DERIVATIVES(T2, id);
CHECK_CHART_DERIVATIVES(T1, T2);
CHECK_CHART_DERIVATIVES(T2, T1);
}
}

6
gtsam/geometry/tests/testSO3.cpp
View File

@ -67,10 +67,12 @@ TEST(SO3, ClosestTo) {
}
//******************************************************************************
namespace {
SO3 id;
Vector3 z_axis(0, 0, 1), v2(1, 2, 0), v3(1, 2, 3);
SO3 R1(Eigen::AngleAxisd(0.1, z_axis));
SO3 R2(Eigen::AngleAxisd(0.2, z_axis));
} // namespace
/* ************************************************************************* */
TEST(SO3, ChordalMean) {
@ -79,16 +81,16 @@ TEST(SO3, ChordalMean) {
}
//******************************************************************************
// Check that Hat specialization is equal to dynamic version
TEST(SO3, Hat) {
// Check that Hat specialization is equal to dynamic version
EXPECT(assert_equal(SO3::Hat(z_axis), SOn::Hat(z_axis)));
EXPECT(assert_equal(SO3::Hat(v2), SOn::Hat(v2)));
EXPECT(assert_equal(SO3::Hat(v3), SOn::Hat(v3)));
}
//******************************************************************************
// Check that Hat specialization is equal to dynamic version
TEST(SO3, Vee) {
// Check that Hat specialization is equal to dynamic version
auto X1 = SOn::Hat(z_axis), X2 = SOn::Hat(v2), X3 = SOn::Hat(v3);
EXPECT(assert_equal(SO3::Vee(X1), SOn::Vee(X1)));
EXPECT(assert_equal(SO3::Vee(X2), SOn::Vee(X2)));

25
gtsam/geometry/tests/testSO4.cpp
View File

@ -48,6 +48,14 @@ TEST(SO4, Concept) {
}
//******************************************************************************
TEST(SO4, Random) {
std::mt19937 rng(42);
auto Q = SO4::Random(rng);
EXPECT_LONGS_EQUAL(4, Q.matrix().rows());
}
//******************************************************************************
namespace {
SO4 id;
Vector6 v1 = (Vector(6) << 0, 0, 0, 0.1, 0, 0).finished();
SO4 Q1 = SO4::Expmap(v1);
@ -55,13 +63,8 @@ Vector6 v2 = (Vector(6) << 0.00, 0.00, 0.00, 0.01, 0.02, 0.03).finished();
SO4 Q2 = SO4::Expmap(v2);
Vector6 v3 = (Vector(6) << 1, 2, 3, 4, 5, 6).finished();
SO4 Q3 = SO4::Expmap(v3);
} // namespace
//******************************************************************************
TEST(SO4, Random) {
std::mt19937 rng(42);
auto Q = SO4::Random(rng);
EXPECT_LONGS_EQUAL(4, Q.matrix().rows());
}
//******************************************************************************
TEST(SO4, Expmap) {
// If we do exponential map in SO(3) subgroup, topleft should be equal to R1.
@ -84,16 +87,16 @@ TEST(SO4, Expmap) {
}
//******************************************************************************
// Check that Hat specialization is equal to dynamic version
TEST(SO4, Hat) {
// Check that Hat specialization is equal to dynamic version
EXPECT(assert_equal(SO4::Hat(v1), SOn::Hat(v1)));
EXPECT(assert_equal(SO4::Hat(v2), SOn::Hat(v2)));
EXPECT(assert_equal(SO4::Hat(v3), SOn::Hat(v3)));
}
//******************************************************************************
// Check that Hat specialization is equal to dynamic version
TEST(SO4, Vee) {
// Check that Hat specialization is equal to dynamic version
auto X1 = SOn::Hat(v1), X2 = SOn::Hat(v2), X3 = SOn::Hat(v3);
EXPECT(assert_equal(SO4::Vee(X1), SOn::Vee(X1)));
EXPECT(assert_equal(SO4::Vee(X2), SOn::Vee(X2)));
@ -116,8 +119,8 @@ TEST(SO4, Retract) {
}
//******************************************************************************
// Check that Cayley is identical to dynamic version
TEST(SO4, Local) {
// Check that Cayley is identical to dynamic version
EXPECT(
assert_equal(id.localCoordinates(Q1), SOn(4).localCoordinates(SOn(Q1))));
EXPECT(
@ -166,9 +169,7 @@ TEST(SO4, vec) {
Matrix actualH;
const Vector16 actual = Q2.vec(actualH);
EXPECT(assert_equal(expected, actual));
std::function<Vector16(const SO4&)> f = [](const SO4& Q) {
return Q.vec();
};
std::function<Vector16(const SO4&)> f = [](const SO4& Q) { return Q.vec(); };
const Matrix numericalH = numericalDerivative11(f, Q2, 1e-5);
EXPECT(assert_equal(numericalH, actualH));
}

150
gtsam/geometry/tests/testTriangulation.cpp
View File

@ -19,6 +19,7 @@
#include <CppUnitLite/TestHarness.h>
#include <gtsam/geometry/Cal3Bundler.h>
#include <gtsam/geometry/Cal3DS2.h>
#include <gtsam/geometry/CameraSet.h>
#include <gtsam/geometry/PinholeCamera.h>
#include <gtsam/geometry/SphericalCamera.h>
@ -38,7 +39,7 @@ using namespace boost::assign;
// Some common constants
static const boost::shared_ptr<Cal3_S2> sharedCal = //
boost::make_shared<Cal3_S2>(1500, 1200, 0, 640, 480);
boost::make_shared<Cal3_S2>(1500, 1200, 0.1, 640, 480);
// Looking along X-axis, 1 meter above ground plane (x-y)
static const Rot3 upright = Rot3::Ypr(-M_PI / 2, 0., -M_PI / 2);
@ -95,11 +96,123 @@ TEST(triangulation, twoPoses) {
EXPECT(assert_equal(Point3(4.995, 0.499167, 1.19814), *actual4, 1e-4));
}
//******************************************************************************
// Simple test with a well-behaved two camera situation with Cal3DS2 calibration.
TEST(triangulation, twoPosesCal3DS2) {
static const boost::shared_ptr<Cal3DS2> sharedDistortedCal = //
boost::make_shared<Cal3DS2>(1500, 1200, 0, 640, 480, -.3, 0.1, 0.0001,
-0.0003);
PinholeCamera<Cal3DS2> camera1Distorted(pose1, *sharedDistortedCal);
PinholeCamera<Cal3DS2> camera2Distorted(pose2, *sharedDistortedCal);
// 0. Project two landmarks into two cameras and triangulate
Point2 z1Distorted = camera1Distorted.project(landmark);
Point2 z2Distorted = camera2Distorted.project(landmark);
vector<Pose3> poses;
Point2Vector measurements;
poses += pose1, pose2;
measurements += z1Distorted, z2Distorted;
double rank_tol = 1e-9;
// 1. Test simple DLT, perfect in no noise situation
bool optimize = false;
boost::optional<Point3> actual1 = //
triangulatePoint3<Cal3DS2>(poses, sharedDistortedCal, measurements,
rank_tol, optimize);
EXPECT(assert_equal(landmark, *actual1, 1e-7));
// 2. test with optimization on, same answer
optimize = true;
boost::optional<Point3> actual2 = //
triangulatePoint3<Cal3DS2>(poses, sharedDistortedCal, measurements,
rank_tol, optimize);
EXPECT(assert_equal(landmark, *actual2, 1e-7));
// 3. Add some noise and try again: result should be ~ (4.995,
// 0.499167, 1.19814)
measurements.at(0) += Point2(0.1, 0.5);
measurements.at(1) += Point2(-0.2, 0.3);
optimize = false;
boost::optional<Point3> actual3 = //
triangulatePoint3<Cal3DS2>(poses, sharedDistortedCal, measurements,
rank_tol, optimize);
EXPECT(assert_equal(Point3(4.995, 0.499167, 1.19814), *actual3, 1e-3));
// 4. Now with optimization on
optimize = true;
boost::optional<Point3> actual4 = //
triangulatePoint3<Cal3DS2>(poses, sharedDistortedCal, measurements,
rank_tol, optimize);
EXPECT(assert_equal(Point3(4.995, 0.499167, 1.19814), *actual4, 1e-3));
}
//******************************************************************************
// Simple test with a well-behaved two camera situation with Fisheye
// calibration.
TEST(triangulation, twoPosesFisheye) {
using Calibration = Cal3Fisheye;
static const boost::shared_ptr<Calibration> sharedDistortedCal = //
boost::make_shared<Calibration>(1500, 1200, .1, 640, 480, -.3, 0.1,
0.0001, -0.0003);
PinholeCamera<Calibration> camera1Distorted(pose1, *sharedDistortedCal);
PinholeCamera<Calibration> camera2Distorted(pose2, *sharedDistortedCal);
// 0. Project two landmarks into two cameras and triangulate
Point2 z1Distorted = camera1Distorted.project(landmark);
Point2 z2Distorted = camera2Distorted.project(landmark);
vector<Pose3> poses;
Point2Vector measurements;
poses += pose1, pose2;
measurements += z1Distorted, z2Distorted;
double rank_tol = 1e-9;
// 1. Test simple DLT, perfect in no noise situation
bool optimize = false;
boost::optional<Point3> actual1 = //
triangulatePoint3<Calibration>(poses, sharedDistortedCal, measurements,
rank_tol, optimize);
EXPECT(assert_equal(landmark, *actual1, 1e-7));
// 2. test with optimization on, same answer
optimize = true;
boost::optional<Point3> actual2 = //
triangulatePoint3<Calibration>(poses, sharedDistortedCal, measurements,
rank_tol, optimize);
EXPECT(assert_equal(landmark, *actual2, 1e-7));
// 3. Add some noise and try again: result should be ~ (4.995,
// 0.499167, 1.19814)
measurements.at(0) += Point2(0.1, 0.5);
measurements.at(1) += Point2(-0.2, 0.3);
optimize = false;
boost::optional<Point3> actual3 = //
triangulatePoint3<Calibration>(poses, sharedDistortedCal, measurements,
rank_tol, optimize);
EXPECT(assert_equal(Point3(4.995, 0.499167, 1.19814), *actual3, 1e-3));
// 4. Now with optimization on
optimize = true;
boost::optional<Point3> actual4 = //
triangulatePoint3<Calibration>(poses, sharedDistortedCal, measurements,
rank_tol, optimize);
EXPECT(assert_equal(Point3(4.995, 0.499167, 1.19814), *actual4, 1e-3));
}
//******************************************************************************
// Similar, but now with Bundler calibration
TEST(triangulation, twoPosesBundler) {
boost::shared_ptr<Cal3Bundler> bundlerCal = //
boost::make_shared<Cal3Bundler>(1500, 0, 0, 640, 480);
boost::make_shared<Cal3Bundler>(1500, 0.1, 0.2, 640, 480);
PinholeCamera<Cal3Bundler> camera1(pose1, *bundlerCal);
PinholeCamera<Cal3Bundler> camera2(pose2, *bundlerCal);
@ -117,7 +230,8 @@ TEST(triangulation, twoPosesBundler) {
double rank_tol = 1e-9;
boost::optional<Point3> actual = //
triangulatePoint3<Cal3Bundler>(poses, bundlerCal, measurements, rank_tol, optimize);
triangulatePoint3<Cal3Bundler>(poses, bundlerCal, measurements, rank_tol,
optimize);
EXPECT(assert_equal(landmark, *actual, 1e-7));
// Add some noise and try again
@ -125,8 +239,9 @@ TEST(triangulation, twoPosesBundler) {
measurements.at(1) += Point2(-0.2, 0.3);
boost::optional<Point3> actual2 = //
triangulatePoint3<Cal3Bundler>(poses, bundlerCal, measurements, rank_tol, optimize);
EXPECT(assert_equal(Point3(4.995, 0.499167, 1.19847), *actual2, 1e-4));
triangulatePoint3<Cal3Bundler>(poses, bundlerCal, measurements, rank_tol,
optimize);
EXPECT(assert_equal(Point3(4.995, 0.499167, 1.19847), *actual2, 1e-3));
}
//******************************************************************************
@ -336,6 +451,31 @@ TEST(triangulation, fourPoses_distinct_Ks) {
#endif
}
//******************************************************************************
TEST(triangulation, fourPoses_distinct_Ks_distortion) {
Cal3DS2 K1(1500, 1200, 0, 640, 480, -.3, 0.1, 0.0001, -0.0003);
// create first camera. Looking along X-axis, 1 meter above ground plane (x-y)
PinholeCamera<Cal3DS2> camera1(pose1, K1);
// create second camera 1 meter to the right of first camera
Cal3DS2 K2(1600, 1300, 0, 650, 440, -.2, 0.05, 0.0002, -0.0001);
PinholeCamera<Cal3DS2> camera2(pose2, K2);
// 1. Project two landmarks into two cameras and triangulate
Point2 z1 = camera1.project(landmark);
Point2 z2 = camera2.project(landmark);
CameraSet<PinholeCamera<Cal3DS2>> cameras;
Point2Vector measurements;
cameras += camera1, camera2;
measurements += z1, z2;
boost::optional<Point3> actual = //
triangulatePoint3<Cal3DS2>(cameras, measurements);
EXPECT(assert_equal(landmark, *actual, 1e-2));
}
//******************************************************************************
TEST(triangulation, outliersAndFarLandmarks) {
Cal3_S2 K1(1500, 1200, 0, 640, 480);

118
gtsam/geometry/triangulation.h
View File

@ -227,6 +227,109 @@ std::vector<Matrix34, Eigen::aligned_allocator<Matrix34>> projectionMatricesFrom
return projection_matrices;
}
/** Create a pinhole calibration from a different Cal3 object, removing
* distortion.
*
* @tparam CALIBRATION Original calibration object.
* @param cal Input calibration object.
* @return Cal3_S2 with only the pinhole elements of cal.
*/
template <class CALIBRATION>
Cal3_S2 createPinholeCalibration(const CALIBRATION& cal) {
const auto& K = cal.K();
return Cal3_S2(K(0, 0), K(1, 1), K(0, 1), K(0, 2), K(1, 2));
}
/** Internal undistortMeasurement to be used by undistortMeasurement and
* undistortMeasurements */
template <class CALIBRATION, class MEASUREMENT>
MEASUREMENT undistortMeasurementInternal(
const CALIBRATION& cal, const MEASUREMENT& measurement,
boost::optional<Cal3_S2> pinholeCal = boost::none) {
if (!pinholeCal) {
pinholeCal = createPinholeCalibration(cal);
}
return pinholeCal->uncalibrate(cal.calibrate(measurement));
}
/** Remove distortion for measurements so as if the measurements came from a
* pinhole camera.
*
* Removes distortion but maintains the K matrix of the initial cal. Operates by
* calibrating using full calibration and uncalibrating with only the pinhole
* component of the calibration.
* @tparam CALIBRATION Calibration type to use.
* @param cal Calibration with which measurements were taken.
* @param measurements Vector of measurements to undistort.
* @return measurements with the effect of the distortion of sharedCal removed.
*/
template <class CALIBRATION>
Point2Vector undistortMeasurements(const CALIBRATION& cal,
const Point2Vector& measurements) {
Cal3_S2 pinholeCalibration = createPinholeCalibration(cal);
Point2Vector undistortedMeasurements;
// Calibrate with cal and uncalibrate with pinhole version of cal so that
// measurements are undistorted.
std::transform(measurements.begin(), measurements.end(),
std::back_inserter(undistortedMeasurements),
[&cal, &pinholeCalibration](const Point2& measurement) {
return undistortMeasurementInternal<CALIBRATION>(
cal, measurement, pinholeCalibration);
});
return undistortedMeasurements;
}
/** Specialization for Cal3_S2 as it doesn't need to be undistorted. */
template <>
inline Point2Vector undistortMeasurements(const Cal3_S2& cal,
const Point2Vector& measurements) {
return measurements;
}
/** Remove distortion for measurements so as if the measurements came from a
* pinhole camera.
*
* Removes distortion but maintains the K matrix of the initial calibrations.
* Operates by calibrating using full calibration and uncalibrating with only
* the pinhole component of the calibration.
* @tparam CAMERA Camera type to use.
* @param cameras Cameras corresponding to each measurement.
* @param measurements Vector of measurements to undistort.
* @return measurements with the effect of the distortion of the camera removed.
*/
template <class CAMERA>
typename CAMERA::MeasurementVector undistortMeasurements(
const CameraSet<CAMERA>& cameras,
const typename CAMERA::MeasurementVector& measurements) {
const size_t num_meas = cameras.size();
assert(num_meas == measurements.size());
typename CAMERA::MeasurementVector undistortedMeasurements(num_meas);
for (size_t ii = 0; ii < num_meas; ++ii) {
// Calibrate with cal and uncalibrate with pinhole version of cal so that
// measurements are undistorted.
undistortedMeasurements[ii] =
undistortMeasurementInternal<typename CAMERA::CalibrationType>(
cameras[ii].calibration(), measurements[ii]);
}
return undistortedMeasurements;
}
/** Specialize for Cal3_S2 to do nothing. */
template <class CAMERA = PinholeCamera<Cal3_S2>>
inline PinholeCamera<Cal3_S2>::MeasurementVector undistortMeasurements(
const CameraSet<PinholeCamera<Cal3_S2>>& cameras,
const PinholeCamera<Cal3_S2>::MeasurementVector& measurements) {
return measurements;
}
/** Specialize for SphericalCamera to do nothing. */
template <class CAMERA = SphericalCamera>
inline SphericalCamera::MeasurementVector undistortMeasurements(
const CameraSet<SphericalCamera>& cameras,
const SphericalCamera::MeasurementVector& measurements) {
return measurements;
}
/**
* Function to triangulate 3D landmark point from an arbitrary number
* of poses (at least 2) using the DLT. The function checks that the
@ -253,8 +356,13 @@ Point3 triangulatePoint3(const std::vector<Pose3>& poses,
// construct projection matrices from poses & calibration
auto projection_matrices = projectionMatricesFromPoses(poses, sharedCal);
// Undistort the measurements, leaving only the pinhole elements in effect.
auto undistortedMeasurements =
undistortMeasurements<CALIBRATION>(*sharedCal, measurements);
// Triangulate linearly
Point3 point = triangulateDLT(projection_matrices, measurements, rank_tol);
Point3 point =
triangulateDLT(projection_matrices, undistortedMeasurements, rank_tol);
// Then refine using non-linear optimization
if (optimize)
@ -300,7 +408,13 @@ Point3 triangulatePoint3(
// construct projection matrices from poses & calibration
auto projection_matrices = projectionMatricesFromCameras(cameras);
Point3 point = triangulateDLT(projection_matrices, measurements, rank_tol);
// Undistort the measurements, leaving only the pinhole elements in effect.
auto undistortedMeasurements =
undistortMeasurements<CAMERA>(cameras, measurements);
Point3 point =
triangulateDLT(projection_matrices, undistortedMeasurements, rank_tol);
// The n refine using non-linear optimization
if (optimize)

16
gtsam/inference/BayesTree-inst.h
View File

@ -95,7 +95,7 @@ namespace gtsam {
/* ************************************************************************* */
template <class CLIQUE>
void BayesTree<CLIQUE>::dot(std::ostream& s, sharedClique clique,
const KeyFormatter& indexFormatter,
const KeyFormatter& keyFormatter,
int parentnum) const {
static int num = 0;
bool first = true;
@ -104,10 +104,10 @@ namespace gtsam {
std::string parent = out.str();
parent += "[label=\"";
for (Key index : clique->conditional_->frontals()) {
if (!first) parent += ",";
for (Key key : clique->conditional_->frontals()) {
if (!first) parent += ", ";
first = false;
parent += indexFormatter(index);
parent += keyFormatter(key);
}
if (clique->parent()) {
@ -116,10 +116,10 @@ namespace gtsam {
}
first = true;
for (Key sep : clique->conditional_->parents()) {
if (!first) parent += ",";
for (Key parentKey : clique->conditional_->parents()) {
if (!first) parent += ", ";
first = false;
parent += indexFormatter(sep);
parent += keyFormatter(parentKey);
}
parent += "\"];\n";
s << parent;
@ -127,7 +127,7 @@ namespace gtsam {
for (sharedClique c : clique->children) {
num++;
dot(s, c, indexFormatter, parentnum);
dot(s, c, keyFormatter, parentnum);
}
}

2
gtsam/inference/BayesTree.h
View File

@ -139,7 +139,7 @@ namespace gtsam {
return nodes_.empty();
}
/** return nodes */
/** Return nodes. Each node is a clique of variables obtained after elimination. */
const Nodes& nodes() const { return nodes_; }
/** Access node by variable */

28
gtsam/inference/ClusterTree-inst.h
View File

@ -15,6 +15,10 @@
#include <gtsam/base/timing.h>
#include <gtsam/base/treeTraversal-inst.h>
#ifdef GTSAM_USE_TBB
#include <mutex>
#endif
namespace gtsam {
/* ************************************************************************* */
@ -120,12 +124,25 @@ struct EliminationData {
size_t myIndexInParent;
FastVector<sharedFactor> childFactors;
boost::shared_ptr<BTNode> bayesTreeNode;
#ifdef GTSAM_USE_TBB
boost::shared_ptr<std::mutex> writeLock;
#endif
EliminationData(EliminationData* _parentData, size_t nChildren) :
parentData(_parentData), bayesTreeNode(boost::make_shared<BTNode>()) {
parentData(_parentData), bayesTreeNode(boost::make_shared<BTNode>())
#ifdef GTSAM_USE_TBB
, writeLock(boost::make_shared<std::mutex>())
#endif
{
if (parentData) {
#ifdef GTSAM_USE_TBB
parentData->writeLock->lock();
#endif
myIndexInParent = parentData->childFactors.size();
parentData->childFactors.push_back(sharedFactor());
#ifdef GTSAM_USE_TBB
parentData->writeLock->unlock();
#endif
} else {
myIndexInParent = 0;
}
@ -196,8 +213,15 @@ struct EliminationData {
nodesIndex_.insert(std::make_pair(j, myData.bayesTreeNode));
// Store remaining factor in parent's gathered factors
if (!eliminationResult.second->empty())
if (!eliminationResult.second->empty()) {
#ifdef GTSAM_USE_TBB
myData.parentData->writeLock->lock();
#endif
myData.parentData->childFactors[myData.myIndexInParent] = eliminationResult.second;
#ifdef GTSAM_USE_TBB
myData.parentData->writeLock->unlock();
#endif
}
}
};
};

4
gtsam/inference/Ordering.cpp
View File

@ -25,11 +25,7 @@
#include <gtsam/3rdparty/CCOLAMD/Include/ccolamd.h>
#ifdef GTSAM_SUPPORT_NESTED_DISSECTION
#ifdef GTSAM_USE_SYSTEM_METIS
#include <metis.h>
#else
#include <gtsam/3rdparty/metis/include/metis.h>
#endif
#endif
using namespace std;

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